# ABigSurvey

**Repository Path**: stevenwu81/ABigSurvey

## Basic Information

- **Project Name**: ABigSurvey
- **Description**: ABigSurvey
Github源地址：https://github.com/NiuTrans/ABigSurvey.git
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-07-17
- **Last Updated**: 2021-07-17

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

 # A Survey of Surveys (NLP & ML)

In this document, we survey hundreds of survey papers on Natural Language  Processing (NLP) and Machine Learning (ML). We categorize these papers into popular topics and do simple counting for some interesting problems. In addition, we show the list of the papers with urls (441 papers). 

## Categorization

We follow the ACL and ICML submission guideline of recent years, covering a broad range of areas in NLP and ML. The categorization is as follows:
+ Natural Language Processing
    + <a href="#computational-social-science-and-social-media">Computational Social Science and Social Media</a>
    + <a href="#dialogue-and-interactive-systems">Dialogue and Interactive Systems</a>
    + <a href="#generation">Generation</a>
    + <a href="#information-extraction">Information Extraction</a>
    + <a href="#information-retrieval-and-text-mining">Information Retrieval and Text Mining</a>
    + <a href="#interpretability-and-analysis-of-models-for-nLP">Interpretability and Analysis of Models for NLP</a>
    + <a href="#knowledge-graph">Knowledge Graph</a>
    + <a href="#language-grounding-to-vision-and-robotics-and-beyond">Language Grounding to Vision, Robotics and Beyond</a>
    + <a href="#linguistic-theories-and-cognitive-modeling-and-psycholinguistics">Linguistic Theories, Cognitive Modeling and Psycholinguistics</a>
    + <a href="#machine-learning-for-nlp">Machine Learning for NLP</a>
    + <a href="#machine-translation">Machine Translation</a>
    + <a href="#natural-language-processing">Natural Language Processing (General)</a>
    + <a href="#ner">Named Entity Recognition (NER)</a>
    + <a href="#nlp-applications">NLP Applications</a>
    + <a href="#question-answering">Question Answering</a>
    + <a href="#reading-comprehension">Reading Comprehension</a>
    + <a href="#recommender-systems">Recommender Systems</a>
    + <a href="#resources-and-evaluation">Resources and Evaluation</a>
    + <a href="#semantics">Semantics</a>
    + <a href="#sentiment-analysis-and-stylistic-analysis-and-argument-mining">Sentiment Analysis, Stylistic Analysis, and Argument Mining</a>
    + <a href="#speech-and-multimodality">Speech and Multimodality</a>
    + <a href="#summarization">Summarization</a>
    + <a href="#tagging-chunking-syntax-and-parsing">Syntax: Tagging, Chunking, Syntax and Parsing</a>
    + <a href="#text-classification">Text Classification</a>
+ Machine Learning
    + <a href="#architectures">Architectures</a>
    + <a href="#automl">AutoML</a>
    + <a href="#bayesian-methods">Bayesian Methods</a>
    + <a href="#classification-clustering-and-regression">Classification,Clustering,Regression</a>
    + <a href="#curriculum-learning">Curriculum Learning</a>
    + <a href="#data-augmentation">Data Augmentation</a>
    + <a href="#deep-learning">Deep Learning - General Methods</a>
    + <a href="#deep-reinforcement-learning">Deep Reinforcement Learning</a>
    + <a href="#federated-learning">Federated Learning</a>
    + <a href="#few-shot-and-zero-shot-learning">Few-Shot and Zero-Shot Learning</a>
    + <a href="#general-machine-learning">General Machine Learning</a>
    + <a href="#generative-adversarial-networks">Generative Adversarial Networks</a>
    + <a href="#graph-neural-networks">Graph Neural Networks</a>
    + <a href="#interpretability-and-analysis">Interpretability and Analysis</a>
    + <a href="#meta-learning">Meta Learning</a>
    + <a href="#metric-learning">Metric Learning</a>
    + <a href="#ml-applications">ML Applications</a>
    + <a href="#model-compression-and-acceleration">Model Compression and Acceleration</a>
    + <a href="#multi-task-and-multi-view-learning">Multi-Task and Multi-View Learning</a>
    + <a href="#online-learning">Online Learning</a>
    + <a href="#optimization">Optimization</a>
    + <a href="#semi-supervised-and-unsupervised-learning">Semi-Supervised and Unsupervised Learning</a>
    + <a href="#transfer-learning">Transfer Learning</a>
    + <a href="#trustworthy-machine-learning">Trustworthy Machine Learning</a>


To reduce class imbalance, we separate some of the hot sub-topics from the original categorization of ACL and ICML submissions. E.g., NER is a first-level area in our categorization because it is the focus of several surveys.

## Statistics

We show the number of paper in each area in Figures 1-2.

<p align="center"><img src="https://i.loli.net/2020/08/10/Sn8FViMlsrjZdCp.png"  width="70%" height="70%" /></p>

<p align="center">Figure 1: # of papers in each NLP area.</p>

<p align="center"><img src="https://i.loli.net/2020/08/10/VLjTobZihYfGaKR.png" width="70%" height="70%" /></p>

<p align="center">Figure 2:  # of papers in each ML area..</p>

Also, we plot paper number as a function of publication year (see Figure 3).

<p align="center"><img src="https://i.loli.net/2020/08/10/ikZXsGYrpfEVA9W.png" width="70%" height="70%"/></p>

<p align="center">Figure 3: # of papers vs publication year.</p>

In addition, we generate word clouds to show hot topics in these surveys (see Figures 4-5).

<p align="center"><img src="https://i.loli.net/2020/07/15/Iywg9lxEGYRvpHO.png" width="70%" height="70%" /></p>

<p align="center">Figure 4: The word cloud for NLP.</p>

<p align="center"><img src="https://i.loli.net/2020/07/15/VYgHR6dhQc2J7Wx.png" width="70%" height="70%" /></p>

<p align="center">Figure 5: The word cloud for ML.</p>


## The NLP Paper List

#### [Computational Social Science and Social Media](#content)

1. **Computational Sociolinguistics: A Survey.** Computational Linguistics 2015 [paper](https://arxiv.org/abs/1508.07544) [bib](/bib/Natural-Language-Processing/Computational-Social-Science-and-Social-Media/Nguyen2015Computational.md)

    *Dong Nguyen, A Seza Dogruoz, Carolyn Penstein Rose, Franciska De Jong*

#### [Dialogue and Interactive Systems](#content)

1. **A Comparative Survey of Recent Natural Language Interfaces for Databases.** VLDB Journal 2019 [paper](https://arxiv.org/abs/1906.08990) [bib](/bib/Natural-Language-Processing/Dialogue-and-Interactive-Systems/Affolter2019A.md)

    *Katrin Affolter, Kurt Stockinger, Abraham Bernstein*

2. **A Survey of Arabic Dialogues Understanding for Spontaneous Dialogues and Instant Message.** International Journal on Natural Language Computing 2015 [paper](https://arxiv.org/abs/1505.03084) [bib](/bib/Natural-Language-Processing/Dialogue-and-Interactive-Systems/Elmadany2015A.md)

    *AbdelRahim A. Elmadany, Sherif M. Abdou, Mervat Gheith*

3. **A Survey of Available Corpora for Building Data-Driven Dialogue Systems.** Computer ence 2017 [paper](https://arxiv.org/abs/1512.05742) [bib](/bib/Natural-Language-Processing/Dialogue-and-Interactive-Systems/Serban2017A.md)

    *Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau*

4. **A Survey of Document Grounded Dialogue Systems.** arXiv 2020 [paper](https://arxiv.org/abs/2004.13818) [bib](/bib/Natural-Language-Processing/Dialogue-and-Interactive-Systems/Ma2020A.md)

    *Longxuan Ma, Wei-Nan Zhang, Mingda Li, Ting Liu*

5. **A Survey of Natural Language Generation Techniques with a Focus on Dialogue Systems - Past, Present and Future Directions.** arXiv 2019 [paper](https://arxiv.org/abs/1906.00500) [bib](/bib/Natural-Language-Processing/Dialogue-and-Interactive-Systems/Santhanam2019A.md)

    *Sashank Santhanam, Samira Shaikh*

6. **A Survey on Dialog Management: Recent Advances and Challenges.** arXiv 2020 [paper](https://arxiv.org/abs/2005.02233) [bib](/bib/Natural-Language-Processing/Dialogue-and-Interactive-Systems/Dai2020A.md)

    *Yinpei Dai, Huihua Yu, Yixuan Jiang, Chengguang Tang, Yongbin Li, Jian Sun*

7. **A Survey on Dialogue Systems: Recent Advances and New Frontiers.** Acm Sigkdd Explorations Newsletter 2017 [paper](https://arxiv.org/abs/1711.01731) [bib](/bib/Natural-Language-Processing/Dialogue-and-Interactive-Systems/Chen2017A.md)

    *Hongshen Chen, Xiaorui Liu, Dawei Yin, Jiliang Tang*

8. **Challenges in Building Intelligent Open-domain Dialog Systems.** ACM Transactions on Information Systems 2020 [paper](https://arxiv.org/abs/1905.05709) [bib](/bib/Natural-Language-Processing/Dialogue-and-Interactive-Systems/Huang2020Challenges.md)

    *Minlie Huang, Xiaoyan Zhu, Jianfeng Gao*

9. **Neural Approaches to Conversational AI.** ACL 2018 [paper](https://arxiv.org/pdf/1809.08267) [bib](/bib/Natural-Language-Processing/Dialogue-and-Interactive-Systems/Gao2018Neural.md)

    *Jianfeng Gao, Michel Galley, Lihong Li*

10. **Recent Advances and Challenges in Task-oriented Dialog System.** Under review of SCIENCE CHINA Technological Science (SCTS) 2020 [paper](https://arxiv.org/pdf/2003.07490) [bib](/bib/Natural-Language-Processing/Dialogue-and-Interactive-Systems/Zhang2020Recent.md)

    *Zheng Zhang, Ryuichi Takanobu, Minlie Huang, Xiaoyan Zhu*

11. **Utterance-level Dialogue Understanding: An Empirical Study.** arXiv 2020 [paper](https://arxiv.org/abs/2009.13902) [bib](/bib/Natural-Language-Processing/Dialogue-and-Interactive-Systems/Ghosal2020Utterance.md)

    *Deepanway Ghosal, Navonil Majumder, Rada Mihalcea, Soujanya Poria*

#### [Generation](#content)

1. **A bit of progress in language modeling.** Computer Speech & Language 2001 [paper](https://arxiv.org/pdf/cs/0108005) [bib](/bib/Natural-Language-Processing/Generation/Goodman2001A.md)

    *Joshua T. Goodman*

2. **A Survey of Paraphrasing and Textual Entailment Methods.** Journal of Artificial Intelligence Research 2010 [paper](https://arxiv.org/abs/0912.3747) [bib](/bib/Natural-Language-Processing/Generation/Androutsopoulos2010A.md)

    *Ion Androutsopoulos, Prodromos Malakasiotis*

3. **A Survey of Knowledge-Enhanced Text Generation.** arXiv 2020 [paper](https://arxiv.org/pdf/2010.04389.pdf) [bib](/bib/Natural-Language-Processing/Generation/Yu2020A.md)

    *Wenhao Yu, Chenguang Zhu, Zaitang Li, Zhiting Hu, Qingyun Wang, Heng Ji, Meng Jiang*

4. **A Survey on Neural Network Language Models.** arXiv 2019 [paper](https://arxiv.org/abs/1906.03591) [bib](/bib/Natural-Language-Processing/Generation/Jing2019A.md)

    *Kun Jing, Jungang Xu*

5. **Evaluation of Text Generation: A Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2006.14799) [bib](/bib/Natural-Language-Processing/Generation/Celikyilmaz2020Evaluation.md)

    *Asli Celikyilmaz, Elizabeth Clark, Jianfeng Gao*

6. **Neural Text Generation: Past, Present and Beyond.** arXiv 2018 [paper](https://arxiv.org/pdf/1803.07133.pdf) [bib](/bib/Natural-Language-Processing/Generation/Lu2018Neural.md)

    *Sidi Lu, Yaoming Zhu, Weinan Zhang, Jun Wang, Yong Yu*

7. **Pre-trained Models for Natural Language Processing : A Survey.** Science China Technological Sciences 2020 [paper](https://arxiv.org/abs/2003.08271) [bib](/bib/Natural-Language-Processing/Generation/Qiu2020Pre.md)

    *Xipeng Qiu, Tianxiang Sun, Yige Xu, Yunfan Shao, Ning Dai, Xuanjing Huang*

8. **Recent Advances in Neural Question Generation.** arXiv 2019 [paper](https://arxiv.org/abs/1905.08949) [bib](/bib/Natural-Language-Processing/Generation/Pan2019Recent.md)

    *Liangming Pan, Wenqiang Lei, Tat-Seng Chua, Min-Yen Kan*

9. **Recent Advances in SQL Query Generation: A Survey.** International Conference on Informatics and Information Technologies 2020 [paper](https://arxiv.org/abs/2005.07667) [bib](/bib/Natural-Language-Processing/Generation/Kalajdjieski2020Recent.md)

    *Jovan Kalajdjieski, Martina Toshevska, Frosina Stojanovska*

10. **Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation.** Journal of Artificial Intelligence Research 2018 [paper](https://arxiv.org/abs/1703.09902) [bib](/bib/Natural-Language-Processing/Generation/Gatt2018Survey.md)

    *Albert Gatt,Emiel Krahmer*

#### [Information Extraction](#content)

1. **A Survey of Deep Learning Methods for Relation Extraction.** arXiv 2017 [paper](https://arxiv.org/abs/1705.03645) [bib](/bib/Natural-Language-Processing/Information-Extraction/Kumar2017A.md)

    *Shantanu Kumar*

2. **A Survey of Event Extraction From Text.** IEEE 2019 [paper](https://ieeexplore.ieee.org/document/8918013) [bib](/bib/Natural-Language-Processing/Information-Extraction/Xiang2019A.md)

    *Wei Xiang, Bang Wang*

3. **A Survey of Neural Network Techniques for Feature Extraction from Text.** arXiv 2017 [paper](https://arxiv.org/abs/1704.08531) [bib](/bib/Natural-Language-Processing/Information-Extraction/John2017A.md)

    *Vineet John*

4. **A Survey on Open Information Extraction.** COLING 2018 [paper](https://arxiv.org/abs/1806.05599) [bib](/bib/Natural-Language-Processing/Information-Extraction/Niklaus2018A.md)

    *Christina Niklaus, Matthias Cetto, André Freitas, Siegfried Handschuh*

5. **A Survey on Temporal Reasoning for Temporal Information Extraction from Text (Extended Abstract).** Journal of Artificial Intelligence Research 2019 [paper](https://arxiv.org/abs/2005.06527) [bib](/bib/Natural-Language-Processing/Information-Extraction/Leeuwenberg2019A.md)

    *Artuur Leeuwenberg, Marie-Francine Moens*

6. **Automatic Extraction of Causal Relations from Natural Language Texts: A Comprehensive Survey.** arXiv 2016 [paper](https://arxiv.org/abs/1605.07895) [bib](/bib/Natural-Language-Processing/Information-Extraction/Asghar2016Automaic.md)

    *Nabiha Asghar*

7. **Content Selection in Data-to-Text Systems: A Survey.** arXiv 2016 [paper](https://arxiv.org/abs/1610.08375) [bib](/bib/Natural-Language-Processing/Information-Extraction/Gkatzia2016Content.md)

    *Dimitra Gkatzia*

8. **Keyphrase Generation: A Multi-Aspect Survey.** FRUCT 2019 [paper](https://arxiv.org/abs/1910.05059) [bib](/bib/Natural-Language-Processing/Information-Extraction/Cano2019Keyphrase.md)

    *Erion Cano, Ondrej Bojar*

9. **More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction.** arXiv 2020 [paper](https://arxiv.org/abs/2004.03186) [bib](/bib/Natural-Language-Processing/Information-Extraction/Han2020More.md)

    *Xu Han, Tianyu Gao, Yankai Lin, Hao Peng, Yaoliang Yang, Chaojun Xiao, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou*

10. **Neural relation extraction: a survey.** arXiv 2020 [paper](https://arxiv.org/abs/2007.04247) [bib](/bib/Natural-Language-Processing/Information-Extraction/Aydar2020Neural.md)

    *Mehmet Aydar, Ozge Bozal,  Furkan Ozbay*

11. **Relation Extraction : A Survey.** arXiv 2017 [paper](https://arxiv.org/abs/1712.05191) [bib](/bib/Natural-Language-Processing/Information-Extraction/Pawar2017Relation.md)

    *Sachin Pawar, Girish K. Palshikar, Pushpak Bhattacharyya*

12. **Short Text Topic Modeling Techniques, Applications, and Performance: A Survey.** arXiv 2019 [paper](https://arxiv.org/abs/1904.07695) [bib](/bib/Natural-Language-Processing/Information-Extraction/Qiang2019Short.md)

    *Jipeng Qiang, Zhenyu Qian, Yun Li, Yunhao Yuan, Xindong Wu*

#### [Information Retrieval and Text Mining](#content)

1. **A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques.** arXiv 2017 [paper](https://arxiv.org/abs/1707.02919) [bib](/bib/Natural-Language-Processing/Information-Retrieval-and-Text-Mining/Allahyari2017A.md)

    *Mehdi Allahyari, Seyed Amin Pouriyeh, Mehdi Assefi, Saied Safaei, Elizabeth D. Trippe, Juan B. Gutierrez, Krys Kochut*

2. **A survey of methods to ease the development of highly multilingual text mining applications.** Language Resources and Evaluation 2012 [paper](https://arxiv.org/abs/1401.2937) [bib](/bib/Natural-Language-Processing/Information-Retrieval-and-Text-Mining/Steinberger2012A.md)

    *Ralf Steinberger*

3. **Opinion Mining and Analysis: A survey.** IJNLC 2013 [paper](https://arxiv.org/abs/1307.3336) [bib](/bib/Natural-Language-Processing/Information-Retrieval-and-Text-Mining/Buche2013Opinion.md)

    *Arti Buche, M. B. Chandak, Akshay Zadgaonkar*

#### [Interpretability and Analysis of Models for NLP](#content)

1. **A Brief Survey and Comparative Study of Recent Development of Pronoun Coreference Resolution.** arxiv 2020 [paper](https://arxiv.org/pdf/2009.12721v1.pdf) [bib](/bib/Natural-Language-Processing/Interpretability-and-Analysis-of-Models-for-NLP/Zhang2020A.md)

    *Hongming Zhang, Xinran Zhao, Yangqiu Song*

2. **A Survey of the State of Explainable AI for Natural Language Processing.** AACL-IJCNLP 2020 [paper](https://arxiv.org/pdf/2010.00711v1.pdf) [bib](/bib/Natural-Language-Processing/Interpretability-and-Analysis-of-Models-for-NLP/Danilevsky2020A.md)

    *Marina Danilevsky, Kun Qian, Ranit Aharonov, Yannis Katsis, Ban Kawas, Prithviraj Sen*

3. **Analysis Methods in Neural Language Processing: A Survey.** NACCL 2018 [paper](https://arxiv.org/abs/1812.08951) [bib](/bib/Natural-Language-Processing/Interpretability-and-Analysis-of-Models-for-NLP/Belinkov2018Analysis.md)

    *Yonatan Belinkov, James R. Glass*

4. **Analyzing and Interpreting Neural Networks for NLP:A Report on the First BlackboxNLP Workshop.** EMNLP 2019 [paper](http://arxiv.org/pdf/1904.04063.pdf) [bib](/bib/Natural-Language-Processing/Interpretability-and-Analysis-of-Models-for-NLP/Alishahi2019Analyzing.md)

    *Afra Alishahi, Grzegorz Chrupala, Tal Linzen*

5. **Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models.** arXiv 2020 [paper](https://arxiv.org/abs/2005.14709) [bib](/bib/Natural-Language-Processing/Interpretability-and-Analysis-of-Models-for-NLP/Schlegel2020Beyond.md)

    *Viktor Schlegel, Goran Nenadic, Riza Batista-Navarro*

6. **Visualizing Natural Language Descriptions: A Survey.** ACM Computing Surveys 2016 [paper](https://arxiv.org/abs/1607.00623) [bib](/bib/Natural-Language-Processing/Interpretability-and-Analysis-of-Models-for-NLP/Hassani2016Visualizing.md)

    *Kaveh Hassani, Won-Sook Lee*

7. **When do Word Embeddings Accurately Reflect Surveys on our Beliefs About People?.** ACL 2020 [paper](https://arxiv.org/abs/2004.12043) [bib](/bib/Natural-Language-Processing/Interpretability-and-Analysis-of-Models-for-NLP/Joseph2020When.md)

    *Kenneth Joseph, Jonathan H. Morgan*

8. **Which BERT? A Survey Organizing Contextualized Encoders.** EMNLP 2020 [paper](https://arxiv.org/pdf/2010.00854.pdf) [bib](/bib/Natural-Language-Processing/Interpretability-and-Analysis-of-Models-for-NLP/Xia2020Which.md)

    *Patrick Xia, Shijie Wu, Benjamin Van Durme*


#### [Knowledge Graph](#content)

1. **A survey of techniques for constructing chinese knowledge graphs and their applications.** Sustainability 2018 [paper](https://www.mdpi.com/2071-1050/10/9/3245/htm) [bib](/bib/Natural-Language-Processing/Knowledge-Graph/Wu2018A.md)

    *Tianxing Wu, Guilin Qi, Cheng Li, Meng Wang*

2. **A Survey on Graph Neural Networks for Knowledge Graph Completion.** arXiv 2020 [paper](https://arxiv.org/abs/2007.12374) [bib](/bib/Natural-Language-Processing/Knowledge-Graph/Siddhant2020A.md)

    *Siddhant Arora*

3. **A Survey on Knowledge Graph-Based Recommender Systems.** arXiv 2020 [paper](https://arxiv.org/abs/2003.00911) [bib](/bib/Natural-Language-Processing/Knowledge-Graph/Guo2020A.md)

    *Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, Qing He*

4. **A Survey on Knowledge Graphs: Representation, Acquisition and Applications.** arXiv 2020 [paper](https://arxiv.org/abs/2002.00388) [bib](/bib/Natural-Language-Processing/Knowledge-Graph/Ji2020A.md)

    *Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, Philip S. Yu*

5. **Knowledge Graph Embedding for Link Prediction: A Comparative Analysis.** arXiv 2016 [paper](https://arxiv.org/abs/2002.00819) [bib](/bib/Natural-Language-Processing/Knowledge-Graph/Rossi2016Knowledge.md)

    *Andrea Rossi, Donatella Firmani, Antonio Matinata, Paolo Merialdo, Denilson Barbosa*

6. **Knowledge Graph Embedding: A Survey of Approaches and Applications.** IEEE 2017 [paper](https://ieeexplore.ieee.org/document/8047276) [bib](/bib/Natural-Language-Processing/Knowledge-Graph/Wang2017Knowledge.md)

    *Quan Wang, Zhendong Mao, Bin Wang, Li Guo*

7. **Knowledge Graphs.** arXiv 2020 [paper](https://arxiv.org/abs/2003.02320) [bib](/bib/Natural-Language-Processing/Knowledge-Graph/Hogan2020Knowledge.md)

    *Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d'Amato, Gerard de Melo, Claudio Gutierrez, José Emilio Labra Gayo, Sabrina Kirrane, Sebastian Neumaier, Axel Polleres, Roberto Navigli, Axel-Cyrille Ngonga Ngomo, Sabbir M. Rashid, Anisa Rula, Lukas Schmelzeisen, Juan F. Sequeda, Steffen Staab, Antoine Zimmermann*

8. **Survey on Domain Knowledge Graph Research.** 计算机系统应用 2020 [paper](http://www.c-s-a.org.cn/html/2020/6/7431.html#top) [bib](/bib/Natural-Language-Processing/Knowledge-Graph/Liu2020Survey.md)

    *刘烨宸, 李华昱*

#### [Language Grounding to Vision and Robotics and Beyond](#content)

1. **Emotionally-Aware Chatbots: A Survey.** arXiv 2018 [paper](https://arxiv.org/abs/1906.09774) [bib](/bib/Natural-Language-Processing/Language-Grounding-to-Vision,-Robotics-and-Beyond/Pamungkas2018Emotionally.md)

    *Endang Wahyu Pamungkas*

2. **Trends in Integration of Vision and Language Research: A Survey of Tasks, Datasets, and Methods.** arXiv 2019 [paper](https://arxiv.org/abs/1907.09358) [bib](/bib/Natural-Language-Processing/Language-Grounding-to-Vision,-Robotics-and-Beyond/Mogadala2019Trends.md)

    *Aditya Mogadala, Marimuthu Kalimuthu, Dietrich Klakow*

#### [Linguistic Theories and Cognitive Modeling and Psycholinguistics](#content)

1. **Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing.** Computational Linguistics 2019 [paper](https://arxiv.org/abs/1807.00914) [bib](/bib/Natural-Language-Processing/Linguistic-Theories,-Cognitive-Modeling-and-Psycholinguistics/Ponti2019Modeling.md)

    *Edoardo Maria Ponti, Helen O'Horan, Yevgeni Berzak, Ivan Vulic, Roi Reichart, Thierry Poibeau, Ekaterina Shutova, Anna Korhonen*

2. **Survey on the Use of Typological Information in Natural Language Processing.** COLING 2016 [paper](https://arxiv.org/abs/1610.03349) [bib](/bib/Natural-Language-Processing/Linguistic-Theories,-Cognitive-Modeling-and-Psycholinguistics/Helen2016Survey.md)

    *Helen O'Horan, Yevgeni Berzak, Ivan Vulic, Roi Reichart, Anna Korhonen*

#### [Machine Learning for NLP](#content)

1. **A comprehensive survey of mostly textual document segmentation algorithms since 2008.** Pattern Recognition 2017 [paper](https://www.sciencedirect.com/science/article/abs/pii/S0031320316303399) [bib](/bib/Natural-Language-Processing/Machine-Learning-for-NLP/Eskenazi2017A.md)

    *Sebastien Eskenazi, Petra Gomez-Krämer, Jean-Marc Ogier*

2. **A Primer on Neural Network Models for Natural Language Processing.** Computer ence 2015 [paper](https://arxiv.org/abs/1510.00726) [bib](/bib/Natural-Language-Processing/Machine-Learning-for-NLP/Goldberg2015A.md)

    *Yoav Goldberg*

3. **A Survey Of Cross-lingual Word Embedding Models.** Journal of Artificial Intelligence Research 2019 [paper](https://arxiv.org/abs/1706.04902) [bib](/bib/Natural-Language-Processing/Machine-Learning-for-NLP/Ruder2019A.md)

    *Sebastian Ruder, Ivan Vulic, Anders Sogaard*

4. **A Survey of Neural Networks and Formal Languages.** arXiv 2020 [paper](https://arxiv.org/abs/2006.01338) [bib](/bib/Natural-Language-Processing/Machine-Learning-for-NLP/Ackerman2020A.md)

    *Joshua Ackerman, George Cybenko*

5. **A Survey of the Usages of Deep Learning in Natural Language Processing.** IEEE 2018 [paper](https://arxiv.org/abs/1807.10854) [bib](/bib/Natural-Language-Processing/Machine-Learning-for-NLP/Otter2018A.md)

    *Daniel W. Otter, Julian R. Medina, Jugal K. Kalita*

6. **A Survey on Contextual Embeddings.** arXiv 2020 [paper](https://arxiv.org/abs/2003.07278) [bib](/bib/Natural-Language-Processing/Machine-Learning-for-NLP/Liu2020A.md)

    *Qi Liu, Matt J. Kusner, Phil Blunsom*

7. **A Survey on Transfer Learning in Natural Language Processing.**  2020 [paper](https://arxiv.org/abs/2007.04239) [bib](/bib/Natural-Language-Processing/Machine-Learning-for-NLP/Alyafeai2020A.md)

    *Alyafeai, Zaid and Alshaibani, Maged Saeed and Ahmad, Irfan*

8. **Adversarial Attacks and Defense on Texts: A Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2005.14108) [bib](/bib/Natural-Language-Processing/Machine-Learning-for-NLP/Huq2020Adversarial.md)

    *Aminul Huq, Mst. Tasnim Pervin*

9. **Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey.** ACM Transactions on Information Systems 2019 [paper](https://arxiv.org/abs/1901.06796) [bib](/bib/Natural-Language-Processing/Machine-Learning-for-NLP/Zhang2019Adversarial.md)

    *Wei Emma Zhang, Quan Z Sheng, Ahoud Alhazmi, Chenliang Li*

10. **An Introductory Survey on Attention Mechanisms in NLP Problems.** IntelliSys 2019 [paper](https://arxiv.org/abs/1811.05544) [bib](/bib/Natural-Language-Processing/Machine-Learning-for-NLP/Hu2019An.md)

    *Dichao Hu*

11. **Attention in Natural Language Processing.** arXiv 2019 [paper](https://arxiv.org/abs/1902.02181v2) [bib](/bib/Natural-Language-Processing/Machine-Learning-for-NLP/Galassi2019Attention.md)

    *Andrea Galassi, Marco Lippi, Paolo Torroni*

12. **From static to dynamic word representations: a survey.** International Journal of Machine Learning and Cybernetics 2020 [paper](http://ir.hit.edu.cn/~car/papers/icmlc2020-wang.pdf) [bib](/bib/Natural-Language-Processing/Machine-Learning-for-NLP/Wang2020From.md)

    *Yuxuan Wang, Yutai Hou, Wanxiang Che, Ting Liu*

13. **From Word to Sense Embeddings: A Survey on Vector Representations of Meaning.** Journal of Artificial Intelligence Research 2018 [paper](https://arxiv.org/abs/1805.04032) [bib](/bib/Natural-Language-Processing/Machine-Learning-for-NLP/Jose2018From.md)

    *Jose Camachocollados, Mohammad Taher Pilehvar*

14. **Natural Language Processing Advancements By Deep Learning: A Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2003.01200) [bib](/bib/Natural-Language-Processing/Machine-Learning-for-NLP/Torfi2020Natural.md)

    *Amirsina Torfi, Rouzbeh A. Shirvani, Yaser Keneshloo, Nader Tavvaf, Edward A. Fox*

15. **Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering.** COLING 2018 [paper](https://arxiv.org/pdf/1806.04330.pdf) [bib](/bib/Natural-Language-Processing/Machine-Learning-for-NLP/Lan2018Neural.md)

    *Wuwei Lan,Wei Xu*

16. **Recent Trends in Deep Learning Based Natural Language Processing.** IEEE 2018 [paper](https://ieeexplore.ieee.org/document/8416973) [bib](/bib/Natural-Language-Processing/Machine-Learning-for-NLP/Young2018Recent.md)

    *Tom Young, Devamanyu Hazarika, Soujanya Poria, Erik Cambria*

17. **Symbolic, Distributed and Distributional Representations for Natural Language Processing in the Era of Deep Learning: a Survey.** Frontiers Robotics AI 2017 [paper](https://arxiv.org/abs/1702.00764) [bib](/bib/Natural-Language-Processing/Machine-Learning-for-NLP/Ferrone2017Symbolic.md)

    *Lorenzo Ferrone, Fabio Massimo Zanzotto*

18. **Syntax Representation in Word Embeddings and Neural Networks -- A Survey.** ITAT 2020 [paper](https://arxiv.org/pdf/2010.01063.pdf) [bib](/bib/Natural-Language-Processing/Machine-Learning-for-NLP/Limisiewicz2020Syntax.md)

    *Tomasz Limisiewicz and David Marecek*

19. **Towards a Robust Deep Neural Network in Texts: A Survey.** arXiv 2020 [paper](https://arxiv.org/abs/1902.07285) [bib](/bib/Natural-Language-Processing/Machine-Learning-for-NLP/Wang2020Towards.md)

    *Wenqi Wang, Lina Wang, Run Wang, Zhibo Wang, Aoshuang Ye*

20. **Word Embeddings: A Survey.** arXiv 2019 [paper](https://arxiv.org/abs/1901.09069) [bib](/bib/Natural-Language-Processing/Machine-Learning-for-NLP/Almeida2019Word.md)

    *Felipe Almeida, Geraldo Xexeo*

#### [Machine Translation](#content)

1. **A Brief Survey of Multilingual Neural Machine Translation.** Computing surveys 2019 [paper](https://arxiv.org/abs/1905.05395) [bib](/bib/Natural-Language-Processing/Machine-Translation/Dabre2019A.md)

    *Raj Dabre, Chenhui Chu, Anoop Kunchukuttan*

2. **A Comprehensive Survey of Multilingual Neural Machine Translation.** Under review at the computing surveys journal 2020 [paper](https://arxiv.org/abs/2001.01115) [bib](/bib/Natural-Language-Processing/Machine-Translation/Dabre2020A.md)

    *Raj Dabre, Chenhui Chu, Anoop Kunchukuttan*

3. **A Survey of Deep Learning Techniques for Neural Machine Translation.** arXiv 2020 [paper](https://arxiv.org/abs/2002.07526) [bib](/bib/Natural-Language-Processing/Machine-Translation/Yang2020A.md)

    *Shuoheng Yang, Yuxin Wang, Xiaowen Chu*

4. **A Survey of Domain Adaptation for Neural Machine Translation.** COLING 2018 [paper](https://arxiv.org/abs/1806.00258) [bib](/bib/Natural-Language-Processing/Machine-Translation/Chu2018A.md)

    *Chenhui Chu, Rui Wang*

5. **A Survey of Methods to Leverage Monolingual Data in Low-resource Neural Machine Translation.** ICATHS 2019 [paper](https://arxiv.org/abs/1910.00373) [bib](/bib/Natural-Language-Processing/Machine-Translation/Gibadullin2019A.md)

    *Ilshat Gibadullin, Aidar Valeev, Albina Khusainova, Adil Mehmood Khan*

6. **A Survey of Multilingual Neural Machine Translation.** Computing Surveys 2020 [paper](https://arxiv.org/abs/1905.05395) [bib](/bib/Natural-Language-Processing/Machine-Translation/Dabre2020Survey.md)

    *Raj Dabre, Chenhui Chu, Anoop Kunchukuttan*

7. **A Survey of Orthographic Information in Machine Translation.** arXiv 2020 [paper](https://arxiv.org/abs/2008.01391) [bib](/bib/Natural-Language-Processing/Machine-Translation/Chakravarthi2020A.md)

    *Bharathi Raja Chakravarthi, Priya Rani, Mihael Arcan, John P. McCrae*

8. **A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena.** Computational Linguistics 2016 [paper](https://arxiv.org/abs/1502.04938) [bib](/bib/Natural-Language-Processing/Machine-Translation/Bisazza2016A.md)

    *Arianna Bisazza, Marcello Federico*

9. **A Survey on Document-level Machine Translation: Methods and Evaluation.** under review at an international journal 2019 [paper](https://arxiv.org/abs/1912.08494) [bib](/bib/Natural-Language-Processing/Machine-Translation/Maruf2019A.md)

    *Sameen Maruf, Fahimeh Saleh, Gholamreza Haffari*

10. **A Survey on Large-scale Machine Learning.** arXiv 2020 [paper](https://arxiv.org/abs/2008.03911) [bib](/bib/Natural-Language-Processing/Machine-Translation/Wang2020A.md)

    *Meng Wang, Weijie Fu, Xiangnan He, Shijie Hao, Xindong Wu*

11. **Machine Translation Approaches and Survey for Indian Languages.** Computational Linguistics 2017 [paper](https://arxiv.org/abs/1701.04290) [bib](/bib/Natural-Language-Processing/Machine-Translation/Khan2017Machine.md)

    *Nadeem Jadoon Khan, Waqas Anwar, Nadir Durrani*

12. **Machine Translation Evaluation Resources and Methods: A Survey.** arXiv 2016 [paper](https://arxiv.org/abs/1605.04515) [bib](/bib/Natural-Language-Processing/Machine-Translation/Han2016Machine.md)

    *Lifeng Han*

13. **Machine Translation using Semantic Web Technologies: A Survey.** Journal of Web Semantics 2018 [paper](https://arxiv.org/abs/1711.09476) [bib](/bib/Natural-Language-Processing/Machine-Translation/Moussallem2018Machine.md)

    *Diego Moussallem, Matthias Wauer, Axelcyrille Ngonga Ngomo*

14. **Machine-Translation History and Evolution: Survey for Arabic-English Translations.** Current Journal of Applied Science & Technology 2017 [paper](https://arxiv.org/abs/1709.04685) [bib](/bib/Natural-Language-Processing/Machine-Translation/Alsohybe2017Machine.md)

    *Nabeel T. Alsohybe, Neama Abdulaziz Dahan, Fadl Mutaher Baalwi*

15. **Multimodal Machine Translation through Visuals and Speech.** Springer 2019 [paper](https://arxiv.org/abs/1911.12798) [bib](/bib/Natural-Language-Processing/Machine-Translation/Sulubacak2019Multimodal.md)

    *Umut Sulubacak, Ozan Caglayan, Stig-Arne Gronroos, Aku Rouhe, Desmond Elliott, Lucia Specia, Jörg Tiedemann*

16. **Neural Machine Translation and Sequence-to-Sequence Models: A Tutorial.** arXiv 2017 [paper](https://arxiv.org/abs/1703.01619) [bib](/bib/Natural-Language-Processing/Machine-Translation/Neubig2017Neural.md)

    *Graham Neubig*

17. **Neural Machine Translation: A Review.** arXiv 2019 [paper](https://arxiv.org/abs/1912.02047) [bib](/bib/Natural-Language-Processing/Machine-Translation/Stahlberg2019Neural.md)

    *Felix Stahlberg*

18. **Neural Machine Translation: Challenges, Progress and Future.** Science China Technological Sciences 2020 [paper](https://arxiv.org/abs/2004.05809) [bib](/bib/Natural-Language-Processing/Machine-Translation/Zhang2020Neural.md)

    *Jiajun Zhang, Chengqing Zong*

19. **The Query Translation Landscape: a Survey.** arXiv 2019 [paper](https://arxiv.org/abs/1910.03118) [bib](/bib/Natural-Language-Processing/Machine-Translation/Mami2019Query.md)

    *Mohamed Nadjib Mami, Damien Graux, Harsh Thakkar, Simon Scerri, Soren Auer, Jens Lehmann*

20. **神经机器翻译前沿综述.** 中文信息学报 2020 [paper](http://124.16.136.79/CN/article/downloadArticleFile.do?attachType=PDF&id=2994) [bib](/bib/Natural-Language-Processing/Machine-Translation/Feng2020Survey.md)

    *冯洋， 邵晨泽*

#### [Natural Language Processing](#content)

1. **A Survey and Classification of Controlled Natural Languages.** Computational Linguistics 2014 [paper](https://arxiv.org/abs/1507.01701) [bib](/bib/Natural-Language-Processing/Natural-Language-Processing/Kuhn2014A.md)

    *Tobias Kuhn*

2. **A Survey on Recognizing Textual Entailment as an NLP Evaluation.** arXiv 2020 [paper](https://arxiv.org/pdf/2010.03061.pdf) [bib](/bib/Natural-Language-Processing/Natural-Language-Processing/Poliak2020A.md)

    *Adam Poliak*

3. **Automatic Arabic Dialect Identification Systems for Written Texts: A Survey.** arxiv 2020 [paper](https://arxiv.org/ftp/arxiv/papers/2009/2009.12622.pdf) [bib](/bib/Natural-Language-Processing/Natural-Language-Processing/Althobaiti2020Automatic.md)

    *Maha J. Althobaiti*

4. **Jumping NLP curves: A review of natural language processing research.** IEEE 2014 [paper](http://krchowdhary.com/ai/ai14/lects/nlp-research-com-intlg-ieee.pdf) [bib](/bib/Natural-Language-Processing/Natural-Language-Processing/Cambria2014Jumping.md)

    *Erik Cambria, Bebo White*

5. **Natural Language Processing - A Survey.** arXiv 2012 [paper](https://arxiv.org/abs/1209.6238) [bib](/bib/Natural-Language-Processing/Natural-Language-Processing/Mote2012Natural.md)

    *Kevin Mote*

6. **Natural Language Processing: State of The Art, Current Trends and Challenges.** arXiv 2017 [paper](https://arxiv.org/abs/1708.05148) [bib](/bib/Natural-Language-Processing/Natural-Language-Processing/Khurana2017Natural.md)

    *Diksha Khurana, Aditya Koli, Kiran Khatter, Sukhdev Singh*

7. **Progress in Neural NLP: Modeling, Learning, and Reasoning.** Engineering 2020 [paper](https://www.sciencedirect.com/science/article/pii/S2095809919304928) [bib](/bib/Natural-Language-Processing/Natural-Language-Processing/Zhou2020Progress.md)

    *Ming Zhou, Nan Duan, Shujie Liu, Heung-Yeung Shum*

8. **Recent trends in deep learning based natural language processing.** ieee 2018 [paper](https://ieeexplore.ieee.org/abstract/document/8416973/) [bib](/bib/Natural-Language-Processing/Natural-Language-Processing/Young2018Recent.md)

    *T Young, D Hazarika, S Poria*

9. **Survey of Network Representation Learning.** Computer Science 2020 [paper](http://www.jsjkx.com/CN/10.11896/jsjkx.190300004) [bib](/bib/Natural-Language-Processing/Natural-Language-Processing/Yu2020Survey.md)

    *Ding Yu, Wei Hao, Pan Zhi-Song, Liu Xin*

#### [NER](#content)

1. **A survey of named entity recognition and classification.** Computational Linguistics 2007 [paper](https://nlp.cs.nyu.edu/sekine/papers/li07.pdf) [bib](/bib/Natural-Language-Processing/Named-Entity-Recognition-(NER)/Nadeau2007A.md)

    *David Nadeau, Satoshi Sekine*

2. **A Survey of Named Entity Recognition in Assamese and other Indian Languages.** arXiv 2014 [paper](https://arxiv.org/abs/1407.2918) [bib](/bib/Natural-Language-Processing/Named-Entity-Recognition-(NER)/Talukdar2014A.md)

    *Gitimoni Talukdar, Pranjal Protim Borah, Arup Baruah*

3. **A Survey on Deep Learning for Named Entity Recognition.** arXiv 2018 [paper](https://arxiv.org/abs/1812.09449) [bib](/bib/Natural-Language-Processing/Named-Entity-Recognition-(NER)/Li2018A.md)

    *Jing Li, Aixin Sun, Jianglei Han, Chenliang Li*

4. **A Survey on Recent Advances in Named Entity Recognition from Deep Learning models.** COLING 2019 [paper](https://arxiv.org/abs/1910.11470) [bib](/bib/Natural-Language-Processing/Named-Entity-Recognition-(NER)/Yadav2019A.md)

    *Vikas Yadav, Steven Bethard*

5. **Design Challenges and Misconceptions in Neural Sequence Labeling.** COLING 2018 [paper](https://arxiv.org/abs/1806.04470) [bib](/bib/Natural-Language-Processing/Named-Entity-Recognition-(NER)/Yang2018Design.md)

    *Jie Yang, Shuailong Liang, Yue Zhang*

6. **Neural Entity Linking: A Survey of Models based on Deep Learning.** arXiv 2020 [paper](https://arxiv.org/abs/2006.00575) [bib](/bib/Natural-Language-Processing/Named-Entity-Recognition-(NER)/Sevgili2020Neural.md)

    *Ozge Sevgili, Artem Shelmanov, Mikhail Arkhipov, Alexander Panchenko, Chris Biemann*

#### [NLP Applications](#content)

1. **A Comprehensive Survey of Grammar Error Correction.** arXiv 2020 [paper](https://arxiv.org/abs/2005.06600) [bib](/bib/Natural-Language-Processing/NLP-Applications/Wang2020A.md)

    *Yu Wang, Yuelin Wang, Jie Liu, Zhuo Liu*

2. **A Short Survey of Biomedical Relation Extraction Techniques.** arXiv 2017 [paper](https://arxiv.org/abs/1707.05850) [bib](/bib/Natural-Language-Processing/NLP-Applications/Shahab2017A.md)

    *Elham Shahab*

3. **A Survey on Assessing the Generalization Envelope of Deep Neural Networks at Inference Time for Image Classification.** arXiv 2020 [paper](https://arxiv.org/abs/2008.09381) [bib](/bib/Natural-Language-Processing/NLP-Applications/Lust2020A.md)

    *Julia Lust, Alexandru Paul Condurache*

4. **A Survey on Natural Language Processing for Fake News Detection.** LREC 2020 [paper](https://arxiv.org/abs/1811.00770) [bib](/bib/Natural-Language-Processing/NLP-Applications/Oshikawa2020A.md)

    *Ray Oshikawa, Jing Qian, William Yang Wang*

5. **A Survey on Text Simplification.** arXiv 2020 [paper](https://arxiv.org/abs/2008.08612) [bib](/bib/Natural-Language-Processing/NLP-Applications/Sikka2020A.md)

    *Punardeep Sikka, Manmeet Singh, Allen Pink, Vijay Mago*

6. **Automatic Language Identification in Texts: A Survey.** Journal of Artificial Intelligence Research 2019 [paper](https://arxiv.org/abs/1804.08186) [bib](/bib/Natural-Language-Processing/NLP-Applications/Jauhiainen2019Automatic.md)

    *Tommi Jauhiainen*

7. **Disinformation Detection: A review of linguistic feature selection and classification models in news veracity assessments.** arXiv 2019 [paper](https://arxiv.org/abs/1910.12073) [bib](/bib/Natural-Language-Processing/NLP-Applications/Tompkins2019Disinformation.md)

    *Jillian Tompkins*

8. **Extraction and Analysis of Fictional Character Networks: A Survey.** ACM Computing Surveys 2019 [paper](https://arxiv.org/abs/1907.02704) [bib](/bib/Natural-Language-Processing/NLP-Applications/(LIA)2019Extraction.md)

    *Xavier Bost (LIA), Vincent Labatut (LIA)*

9. **Fake News Detection using Stance Classification: A Survey.** arXiv 2019 [paper](https://arxiv.org/abs/1907.00181) [bib](/bib/Natural-Language-Processing/NLP-Applications/Lillie2019Fake.md)

    *Anders Edelbo Lillie, Emil Refsgaard Middelboe*

10. **Fake News: A Survey of Research, Detection Methods, and Opportunities.** ACM 2018 [paper](https://arxiv.org/abs/1812.00315) [bib](/bib/Natural-Language-Processing/NLP-Applications/Zhou2018Fake.md)

    *Xinyi Zhou, Reza Zafarani*

11. **Image Captioning based on Deep Learning Methods: A Survey.** arXiv 2019 [paper](https://arxiv.org/abs/1905.08110) [bib](/bib/Natural-Language-Processing/NLP-Applications/Wang2019Image.md)

    *Yiyu Wang, Jungang Xu, Yingfei Sun, Ben He*

12. **Referring Expression Comprehension: A Survey of Methods and Datasets.** arXiv 2020 [paper](https://arxiv.org/abs/2007.09554) [bib](/bib/Natural-Language-Processing/NLP-Applications/Qiao2020Referring.md)

    *Yanyuan Qiao, Chaorui Deng, Qi Wu*

13. **SECNLP: A Survey of Embeddings in Clinical Natural Language Processing.** Journal of Biomedical Informatics 2019 [paper](https://www.sciencedirect.com/science/article/pii/S1532046419302436) [bib](/bib/Natural-Language-Processing/NLP-Applications/KS2019SECNLP.md)

    *Kalyan KS, S Sangeetha*

14. **Survey of Text-based Epidemic Intelligence: A Computational Linguistic Perspective.** ACM Computing Surveys 2019 [paper](https://arxiv.org/abs/1903.05801) [bib](/bib/Natural-Language-Processing/NLP-Applications/Joshi2019Survey.md)

    *Aditya Joshi, Sarvnaz Karimi, Ross Sparks, Cecile Paris, C Raina MacIntyre*

15. **Survey on Publicly Available Sinhala Natural Language Processing Tools and Research.** arxiv 2020 [paper](https://arxiv.org/pdf/1906.02358v6.pdf) [bib](/bib/Natural-Language-Processing/NLP-Applications/Silva2020Survey.md)

    *Nisansa de Silva*

16. **Text Detection and Recognition in the Wild: A Review.** arXiv 2020 [paper](https://arxiv.org/abs/2006.04305) [bib](/bib/Natural-Language-Processing/NLP-Applications/Raisi2020Text.md)

    *Zobeir Raisi, Mohamed A. Naiel, Paul Fieguth, Steven Wardell, John Zelek*

17. **Text Recognition in the Wild: A Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2005.03492) [bib](/bib/Natural-Language-Processing/NLP-Applications/Chen2020Text.md)

    *Xiaoxue Chen, Lianwen Jin, Yuanzhi Zhu, Canjie Luo, Tianwei Wang*

18. **Towards Improved Model Design for Authorship Identification: A Survey on Writing Style Understanding.** arxiv 2020 [paper](https://arxiv.org/pdf/2009.14445v1.pdf) [bib](/bib/Natural-Language-Processing/NLP-Applications/Ma2020Towards.md)

    *Weicheng Ma, Ruibo Liu, Lili Wang, Soroush Vosoughi*

#### [Question Answering](#content)

1. **A Survey on Complex Question Answering over Knowledge Base: Recent Advances and Challenges.**  2020 [paper](https://arxiv.org/abs/2007.13069) [bib](/bib/Natural-Language-Processing/Question-Answering/Fu2020A.md)

    *Bin Fu, Yunqi Qiu, Chengguang Tang, Yang Li, Haiyang Yu, Jian Sun*

2. **A survey on question answering technology from an information retrieval perspective.** Information ences 2011 [paper](https://www.sciencedirect.com/science/article/pii/S0020025511003860) [bib](/bib/Natural-Language-Processing/Question-Answering/Kolomiyets2011A.md)

    *Oleksandr Kolomiyets, Marie-Francine Moens*

3. **A Survey on Why-Type Question Answering Systems.** arXiv 2019 [paper](https://arxiv.org/abs/1911.04879) [bib](/bib/Natural-Language-Processing/Question-Answering/Breja2019A.md)

    *Manvi Breja, Sanjay Kumar Jain*

4. **Core techniques of question answering systems over knowledge bases: a survey.** Knowledge and Information Systems 2017 [paper](https://link.springer.com/article/10.1007/s10115-017-1100-y) [bib](/bib/Natural-Language-Processing/Question-Answering/Diefenbach2017Core.md)

    *Dennis Diefenbach, Vanessa Lopez, Kamal Singh & Pierre Maret*

5. **Introduction to Neural Network based Approaches for Question Answering over Knowledge Graphs.** arXiv 2019 [paper](https://arxiv.org/abs/1907.09361) [bib](/bib/Natural-Language-Processing/Question-Answering/Chakraborty2019Introduction.md)

    *Nilesh Chakraborty,Denis Lukovnikov,Gaurav Maheshwari,Priyansh Trivedi,Jens Lehmann,Asja Fischer*

6. **Survey of Visual Question Answering: Datasets and Techniques.** arXiv 2017 [paper](https://arxiv.org/abs/1705.03865) [bib](/bib/Natural-Language-Processing/Question-Answering/Gupta2017Survey.md)

    *Akshay Kumar Gupta*

7. **Text-based Question Answering from Information Retrieval and Deep Neural Network Perspectives: A Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2002.06612) [bib](/bib/Natural-Language-Processing/Question-Answering/Abbasiyantaeb2020Text-based.md)

    *Zahra Abbasiyantaeb, Saeedeh Momtazi*

8. **Tutorial on Answering Questions about Images with Deep Learning.** Summer School on Integrating Vision and Language: Deep Learning 2016 [paper](https://arxiv.org/abs/1610.01076) [bib](/bib/Natural-Language-Processing/Question-Answering/Malinowski2016Tutorial.md)

    *Mateusz Malinowski, Mario Fritz*

9. **Visual Question Answering using Deep Learning: A Survey and Performance Analysis.** arXiv 2019 [paper](https://arxiv.org/abs/1909.01860) [bib](/bib/Natural-Language-Processing/Question-Answering/Srivastava2019Visual.md)

    *Yash Srivastava, Vaishnav Murali, Shiv Ram Dubey, Snehasis Mukherjee*

#### [Reading Comprehension](#content)

1. **A Survey on Machine Reading Comprehension Systems.** arXiv 2020 [paper](https://arxiv.org/abs/2001.01582) [bib](/bib/Natural-Language-Processing/Reading-Comprehension/Baradaran2020A.md)

    *Razieh Baradaran, Razieh Ghiasi, Hossein Amirkhani*

2. **A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics, and Benchmark Datasets.**  2020 [paper](https://arxiv.org/abs/2006.11880) [bib](/bib/Natural-Language-Processing/Reading-Comprehension/Zeng2020A.md)

    *Chengchang Zeng, Shaobo Li, Qin Li, Jie Hu, Jianjun Hu*

3. **A Survey on Neural Machine Reading Comprehension.** arXiv 2019 [paper](https://arxiv.org/abs/1906.03824) [bib](/bib/Natural-Language-Processing/Reading-Comprehension/Qiu2019A.md)

    *Boyu Qiu, Xu Chen, Jungang Xu, Yingfei Sun*

4. **Machine Reading Comprehension: a Literature Review.** arXiv 2019 [paper](https://arxiv.org/abs/1907.01686) [bib](/bib/Natural-Language-Processing/Reading-Comprehension/Zhang2019Machine.md)

    *Xin Zhang, An Yang, Sujian Li, Yizhong Wang*

5. **Machine Reading Comprehension: The Role of Contextualized Language Models and Beyond.** Computational Linguistics 2020 [paper](https://arxiv.org/abs/2005.06249) [bib](/bib/Natural-Language-Processing/Reading-Comprehension/Zhang2020Machine.md)

    *Zhuosheng Zhang, Hai Zhao, Rui Wang*

6. **Neural Machine Reading Comprehension: Methods and Trends.** Applied ences 2019 [paper](https://arxiv.org/abs/1907.01118) [bib](/bib/Natural-Language-Processing/Reading-Comprehension/Liu2019Neural.md)

    *Shanshan Liu, Xin Zhang, Sheng Zhang, Hui Wang, Weiming Zhang*

#### [Recommender Systems](#content)

1. **A review on deep learning for recommender systems: challenges and remedies.** Artificial Intelligence Review 2019 [paper](https://link.springer.com/article/10.1007/s10462-018-9654-y) [bib](/bib/Natural-Language-Processing/Recommender-Systems/Batmaz2019A.md)

    *Zeynep Batmaz, Ali Yurekli, Alper Bilge, Cihan Kaleli*

2. **A Survey on Knowledge Graph-Based Recommender Systems.** arXiv 2020 [paper](https://arxiv.org/abs/2003.00911) [bib](/bib/Natural-Language-Processing/Recommender-Systems/Guo2020A.md)

    *Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, Qing He*

3. **Adversarial Machine Learning in Recommender Systems:State of the art and Challenges.** arXiv 2020 [paper](https://arxiv.org/abs/2005.10322) [bib](/bib/Natural-Language-Processing/Recommender-Systems/Deldjoo2020Adversarial.md)

    *Yashar Deldjoo, Tommaso Di Noia, Felice Antonio Merra*

4. **Cross Domain Recommender Systems: A Systematic Literature Review.** ACM Computing Surveys 2017 [paper](https://dl.acm.org/doi/10.1145/3073565) [bib](/bib/Natural-Language-Processing/Recommender-Systems/Khan2017Cross.md)

    *Muhammad Murad Khan,Roliana Ibrahim,Imran Ghani*

5. **Deep Learning based Recommender System: A Survey and New Perspectives.** ACM Computing Surveys 2019 [paper](https://arxiv.org/abs/1707.07435) [bib](/bib/Natural-Language-Processing/Recommender-Systems/Zhang2019Deep.md)

    *Shuai Zhang, Lina Yao, Aixin Sun, Yi Tay*

6. **Deep Learning on Knowledge Graph for Recommender System: A Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2004.00387) [bib](/bib/Natural-Language-Processing/Recommender-Systems/Gao2020Deep.md)

    *Yang Gao, Yi-Fan Li, Yu Lin, Hang Gao, Latifur Khan*

7. **Explainable Recommendation: A Survey and New Perspectives.** Foundations and Trends in Information Retrieval 2020 [paper](https://arxiv.org/abs/1804.11192) [bib](/bib/Natural-Language-Processing/Recommender-Systems/Zhang2020Explainable.md)

    *Yongfeng Zhang, Xu Chen*

8. **Sequence-Aware Recommender Systems.** ACM Computing Surveys 2018 [paper](https://arxiv.org/abs/1802.08452) [bib](/bib/Natural-Language-Processing/Recommender-Systems/Quadrana2018Sequence-Aware.md)

    *Massimo Quadrana,Paolo Cremonesi,Dietmar Jannach*

9. **Trust in Recommender Systems: A Deep Learning Perspective.** arxiv 2020 [paper](http://arxiv.org/abs/2004.03774) [bib](/bib/Natural-Language-Processing/Recommender-Systems/Dong2020Trust.md)

    *Manqing Dong, Feng Yuan, Lina Yao, Xianzhi Wang, Xiwei Xu, Liming Zhu*

10. **Use of Deep Learning in Modern Recommendation System: A Summary of Recent Works.** International Journal of Computer Applications 2017 [paper](https://arxiv.org/abs/1712.07525) [bib](/bib/Natural-Language-Processing/Recommender-Systems/Singhal2017Use.md)

    *Ayush Singhal, Pradeep Sinha, Rakesh Pant*

#### [Resources and Evaluation](#content)

1. **A Short Survey on Sense-Annotated Corpora.** International Conference on Language Resources and Evaluation 2020 [paper](https://arxiv.org/abs/1802.04744) [bib](/bib/Natural-Language-Processing/Resources-and-Evaluation/Pasini2020A.md)

    *Tommaso Pasini, José Camacho-Collados*

2. **A Survey of Current Datasets for Vision and Language Research.** EMNLP 2015 [paper](https://arxiv.org/abs/1506.06833) [bib](/bib/Natural-Language-Processing/Resources-and-Evaluation/Ferraro2015A.md)

    *Francis Ferraro, Nasrin Mostafazadeh, Ting-Hao (Kenneth) Huang, Lucy Vanderwende, Jacob Devlin, Michel Galley, Margaret Mitchell*

3. **A Survey of Evaluation Metrics Used for NLG Systems.** arXiv 2020 [paper](https://arxiv.org/abs/2008.12009) [bib](/bib/Natural-Language-Processing/Resources-and-Evaluation/Sai2020A.md)

    *Ananya B. Sai, Akash Kumar Mohankumar, Mitesh M. Khapra*

4. **A Survey of Word Embeddings Evaluation Methods.** arXiv 2018 [paper](https://arxiv.org/abs/1801.09536) [bib](/bib/Natural-Language-Processing/Resources-and-Evaluation/Bakarov2018A.md)

    *Amir Bakarov*

5. **A Survey on Recognizing Textual Entailment as an NLP Evaluation.** EMNLP 2020 [paper](https://arxiv.org/pdf/2010.03061.pdf) [bib](/bib/Natural-Language-Processing/Resources-and-Evaluation/Bakarov2018A.md)

    *Adam Poliak*

6. **Critical Survey of the Freely Available Arabic Corpora.** International Conference on Language Resources and Evaluation 2017 [paper](https://arxiv.org/abs/1702.07835) [bib](/bib/Natural-Language-Processing/Resources-and-Evaluation/Zaghouani2017Critical.md)

    *Wajdi Zaghouani*

7. **Distributional Measures of Semantic Distance: A Survey.** arXiv 2012 [paper](https://arxiv.org/abs/1203.1858) [bib](/bib/Natural-Language-Processing/Resources-and-Evaluation/Mohammad2012Distributional.md)

    *Saif Mohammad, Graeme Hirst*

8. **Measuring Sentences Similarity: A Survey.** Indian Journal of Science and Technology 2019 [paper](https://arxiv.org/abs/1910.03940) [bib](/bib/Natural-Language-Processing/Resources-and-Evaluation/Farouk2019Measuring.md)

    *Mamdouh Farouk*

9. **Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches.** JAIR 2020 [paper](https://arxiv.org/abs/1904.01172) [bib](/bib/Natural-Language-Processing/Resources-and-Evaluation/Storks2020Recent.md)

    *Shane Storks, Qiaozi Gao, Joyce Y. Chai*

10. **Survey on Evaluation Methods for Dialogue Systems.** Artificial Intelligence Review 2019 [paper](https://arxiv.org/abs/1905.04071) [bib](/bib/Natural-Language-Processing/Resources-and-Evaluation/Deriu2019Survey.md)

    *Jan Deriu, Alvaro Rodrigo, Arantxa Otegi, Guillermo Echegoyen, Sophie Rosset, Eneko Agirre, Mark Cieliebak*

11. **Survey on Publicly Available Sinhala Natural Language Processing Tools and Research.** arXiv 2019 [paper](https://arxiv.org/abs/1906.02358) [bib](/bib/Natural-Language-Processing/Resources-and-Evaluation/Silva2019Survey.md)

    *Nisansa de Silva*

#### [Semantics](#content)

1. **A survey of loss functions for semantic segmentation.** arXiv 2020 [paper](https://arxiv.org/abs/2006.14822) [bib](/bib/Natural-Language-Processing/Semantics/Jadon2020A.md)

    *Shruti Jadon*

2. **Diachronic word embeddings and semantic shifts: a survey.** COLING 2018 [paper](https://arxiv.org/abs/1806.03537) [bib](/bib/Natural-Language-Processing/Semantics/Kutuzov2018Diachronic.md)

    *Andrey Kutuzov, Lilja Ovrelid, Terrence Szymanski, Erik Velldal*

3. **Evolution of Semantic Similarity -- A Survey.** ACM Computing Surveys 2020 [paper](https://arxiv.org/abs/2004.13820) [bib](/bib/Natural-Language-Processing/Semantics/Chandrasekaran2020Evolution.md)

    *Dhivya Chandrasekaran, Vijay Mago*

4. **Semantic search on text and knowledge bases.** Foundations and trends in information retrieval 2016 [paper](https://www.researchgate.net/profile/Hannah_Bast/publication/304364705_Semantic_Search_on_Text_and_Knowledge_Bases/links/594a4734aca2723195de48df/Semantic-Search-on-Text-and-Knowledge-Bases.pdf) [bib](/bib/Natural-Language-Processing/Semantics/Bast2016Semantic.md)

    *Hannah Bast ,  Bjorn Buchhold,  Elmar Haussmann*

5. **Semantics, Modelling, and the Problem of Representation of Meaning -- a Brief Survey of Recent Literature.** arXiv 2014 [paper](https://arxiv.org/abs/1402.7265) [bib](/bib/Natural-Language-Processing/Semantics/Gal2014Semantics,.md)

    *Yarin Gal*

6. **Survey of Computational Approaches to Lexical Semantic Change.** arXiv 2019 [paper](https://arxiv.org/abs/1811.06278) [bib](/bib/Natural-Language-Processing/Semantics/Tahmasebi2019Survey.md)

    *Nina Tahmasebi, Lars Borin, Adam Jatowt*

7. **The Knowledge Acquisition Bottleneck Problem in Multilingual Word Sense Disambiguation.** IJCAI 2020 [paper](https://www.ijcai.org/Proceedings/2020/687) [bib](/bib/Natural-Language-Processing/Semantics/Pasini2020The.md)

    *Tommaso Pasini*

8. **Word sense disambiguation: a survey.** International Journal of Control Theory and Computer Modeling 2015 [paper](https://arxiv.org/abs/1508.01346) [bib](/bib/Natural-Language-Processing/Semantics/Pal2015Word.md)

    *Alok Ranjan Pal, Diganta Saha*

#### [Sentiment Analysis and Stylistic Analysis and Argument Mining](#content)

1. **A Comprehensive Survey on Aspect Based Sentiment Analysis.** arXiv 2020 [paper](https://arxiv.org/abs/2006.04611) [bib](/bib/Natural-Language-Processing/Sentiment-Analysis,-Stylistic-Analysis,-and-Argument-Mining/Yadav2020A.md)

    *Kaustubh Yadav*

2. **A Survey on Sentiment and Emotion Analysis for Computational Literary Studies.** ZFDG 2018 [paper](https://arxiv.org/abs/1808.03137) [bib](/bib/Natural-Language-Processing/Sentiment-Analysis,-Stylistic-Analysis,-and-Argument-Mining/Kim2018A.md)

    *Evgeny Kim, Roman Klinger*

3. **Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research.** arXiv 2020 [paper](https://arxiv.org/abs/2005.00357v1) [bib](/bib/Natural-Language-Processing/Sentiment-Analysis,-Stylistic-Analysis,-and-Argument-Mining/Poria2020Beneath.md)

    *Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, Rada Mihalcea*

4. **Deep Learning for Aspect-Level Sentiment Classification: Survey, Vision, and Challenges.** IEEE 2019 [paper](https://ieeexplore.ieee.org/document/8726353) [bib](/bib/Natural-Language-Processing/Sentiment-Analysis,-Stylistic-Analysis,-and-Argument-Mining/Zhou2019Deep.md)

    *Jie Zhou, Jimmy Xiangji Huang, Qin Chen, Qinmin Vivian Hu, Tingting Wang, Liang He*

5. **Deep Learning for Sentiment Analysis : A Survey.** Wiley Interdisciplinary Reviews: Data Mining and Knowledge 2018 [paper](https://arxiv.org/abs/1801.07883) [bib](/bib/Natural-Language-Processing/Sentiment-Analysis,-Stylistic-Analysis,-and-Argument-Mining/Zhang2018Deep.md)

    *Lei Zhang, Shuai Wang, Bing Liu*

6. **Sentiment analysis for Arabic language: A brief survey of approaches and techniques.** arXiv 2018 [paper](https://arxiv.org/abs/1809.02782) [bib](/bib/Natural-Language-Processing/Sentiment-Analysis,-Stylistic-Analysis,-and-Argument-Mining/Alrefai2018Sentiment.md)

    *Mo'ath Alrefai, Hossam Faris, Ibrahim Aljarah*

7. **Sentiment Analysis of Czech Texts: An Algorithmic Survey.** International Conference on Agents and Artificial Intelligence 2019 [paper](https://arxiv.org/abs/1901.02780) [bib](/bib/Natural-Language-Processing/Sentiment-Analysis,-Stylistic-Analysis,-and-Argument-Mining/Cano2019Sentiment.md)

    *Erion Cano, Ondřej Bojar*

8. **Sentiment Analysis of Twitter Data: A Survey of Techniques.** International Journal of Computer Applications 2016 [paper](https://arxiv.org/abs/1601.06971) [bib](/bib/Natural-Language-Processing/Sentiment-Analysis,-Stylistic-Analysis,-and-Argument-Mining/Kharde2016Sentiment.md)

    *Vishal.A.Kharde, Prof. Sheetal.Sonawane*

9. **Sentiment Analysis on YouTube: A Brief Survey.** MAGNT Research Report 2015 [paper](https://arxiv.org/abs/1511.09142) [bib](/bib/Natural-Language-Processing/Sentiment-Analysis,-Stylistic-Analysis,-and-Argument-Mining/Asghar2015Sentiment.md)

    *Muhammad Zubair Asghar, Shakeel Ahmad, Afsana Marwat, Fazal Masud Kundi*

10. **Sentiment/Subjectivity Analysis Survey for Languages other than English.** Social Network Analysis & Mining 2016 [paper](https://arxiv.org/abs/1601.00087) [bib](/bib/Natural-Language-Processing/Sentiment-Analysis,-Stylistic-Analysis,-and-Argument-Mining/Korayem2016SentimentSubjectivity.md)

    *Mohammed Korayem, Khalifeh Aljadda, David Crandall*

11. **Word Embeddings for Sentiment Analysis: A Comprehensive Empirical Survey.** arXiv 2019 [paper](https://arxiv.org/abs/1902.00753) [bib](/bib/Natural-Language-Processing/Sentiment-Analysis,-Stylistic-Analysis,-and-Argument-Mining/Cano2019Word.md)

    *Erion Cano, Maurizio Morisio*

#### [Speech and Multimodality](#content)

1. **A Comprehensive Survey on Cross-modal Retrieval.** arXiv 2016 [paper](https://arxiv.org/abs/1607.06215) [bib](/bib/Natural-Language-Processing/Speech-and-Multimodality/Wang2016A.md)

    *Kaiye Wang*

2. **A Multimodal Memes Classification: A Survey and Open Research Issues.** arXiv 2020 [paper](https://arxiv.org/abs/2009.08395) [bib](/bib/Natural-Language-Processing/Speech-and-Multimodality/Afridi2020A.md)

    *Tariq Habib Afridi, Aftab Alam, Muhammad Numan Khan, Jawad Khan, Young-Koo Lee*

3. **A Survey and Taxonomy of Adversarial Neural Networks for Text-to-Image Synthesis.** Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2019 [paper](https://arxiv.org/abs/1910.09399) [bib](/bib/Natural-Language-Processing/Speech-and-Multimodality/Agnese2019A.md)

    *Jorge Agnese, Jonathan Herrera, Haicheng Tao, Xingquan Zhu*

4. **A Survey of Code-switched Speech and Language Processing.** Elsevier 2019 [paper](https://arxiv.org/abs/1904.00784) [bib](/bib/Natural-Language-Processing/Speech-and-Multimodality/Sitaram2019A.md)

    *Sunayana Sitaram, Khyathi Raghavi Chandu, Sai Krishna Rallabandi, Alan W. Black*

5. **A Survey of Recent DNN Architectures on the TIMIT Phone Recognition Task.** TSD 2018 [paper](https://arxiv.org/abs/1806.07974) [bib](/bib/Natural-Language-Processing/Speech-and-Multimodality/Michalek2018A.md)

    *Josef Michalek, Jan Vanek*

6. **A Survey of Voice Translation Methodologies - Acoustic Dialect Decoder.** International Conference on Information 2016 [paper](https://arxiv.org/abs/1610.03934) [bib](/bib/Natural-Language-Processing/Speech-and-Multimodality/Krupakar2016A.md)

    *Hans Krupakar, Keerthika Rajvel, Bharathi B, Angel Deborah S, Vallidevi Krishnamurthy*

7. **Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures.** IJCAI 2017 [paper](https://arxiv.org/abs/1601.03896) [bib](/bib/Natural-Language-Processing/Speech-and-Multimodality/Bernardi2017Automatic.md)

    *Raffaella Bernardi, Ruket Cakici, Desmond Elliott, Aykut Erdem, Erkut Erdem, Nazli Ikizler-Cinbis, Frank Keller, Adrian Muscat, Barbara Plank*

8. **Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems.** arXiv 2019 [paper](https://arxiv.org/abs/1903.12394) [bib](/bib/Natural-Language-Processing/Speech-and-Multimodality/Rueden2019Informed.md)

    *Laura von Rueden, Sebastian Mayer, Katharina Beckh, Bogdan Georgiev, Sven Giesselbach, Raoul Heese, Birgit Kirsch, Julius Pfrommer, Annika Pick, Rajkumar Ramamurthy, Michal Walczak, Jochen Garcke, Christian Bauckhage, Jannis Schuecker*

9. **Multimodal Machine Learning: A Survey and Taxonomy.** IEEE 2019 [paper](https://arxiv.org/abs/1705.09406) [bib](/bib/Natural-Language-Processing/Speech-and-Multimodality/Baltrusaitis2019Multimodal.md)

    *Tadas Baltrusaitis, Chaitanya Ahuja, Louis-Philippe Morency*

10. **Speech and Language Processing.** Speech and language processing 2019 [paper](http://web.stanford.edu/~jurafsky/slp3/) [bib](/bib/Natural-Language-Processing/Speech-and-Multimodality/Martin2019Speech.md)

    *Dan Jurafsky and James H. Martin*

#### [Summarization](#content)

1. **A Survey on Neural Network-Based Summarization Methods.** arXiv 2018 [paper](https://arxiv.org/abs/1804.04589) [bib](/bib/Natural-Language-Processing/Summarization/Dong2018A.md)

    *Yue Dong*

2. **Abstractive Summarization: A Survey of the State of the Art.** AAAI 2019 [paper](https://aaai.org/ojs/index.php/AAAI/article/view/5056) [bib](/bib/Natural-Language-Processing/Summarization/Lin2019Abstractive.md)

    *Hui Lin, Vincent Ng*

3. **Automated text summarisation and evidence-based medicine: A survey of two domains.** arXiv 2017 [paper](https://arxiv.org/abs/1706.08162) [bib](/bib/Natural-Language-Processing/Summarization/Sarker2017Automated.md)

    *Abeed Sarker, Diego Molla Aliod, Cecile Paris*

4. **Automatic Keyword Extraction for Text Summarization: A Survey.** arXiv 2017 [paper](https://arxiv.org/abs/1704.03242) [bib](/bib/Natural-Language-Processing/Summarization/Bharti2017Automatic.md)

    *Santosh Kumar Bharti, Korra Sathya Babu*

5. **From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information.** IJCAI 2020 [paper](https://arxiv.org/abs/2005.04684) [bib](/bib/Natural-Language-Processing/Summarization/Gao2020From.md)

    *Shen Gao, Xiuying Chen, Zhaochun Ren, Dongyan Zhao, Rui Yan*

6. **Neural Abstractive Text Summarization with Sequence-to-Sequence Models: A Survey.** arXiv 2018 [paper](https://arxiv.org/abs/1812.02303) [bib](/bib/Natural-Language-Processing/Summarization/Shi2018Neural.md)

    *Tian Shi, Yaser Keneshloo, Naren Ramakrishnan, Chandan K. Reddy*

7. **Recent automatic text summarization techniques: a survey.** Artificial Intelligence Review 2016 [paper](https://link.springer.com/article/10.1007%2Fs10462-016-9475-9) [bib](/bib/Natural-Language-Processing/Summarization/Gambhir2016Recent.md)

    *Mahak Gambhir, Vishal Gupta*

8. **Text Summarization Techniques: A Brief Survey.** IJCAI 2017 [paper](https://arxiv.org/abs/1707.02268) [bib](/bib/Natural-Language-Processing/Summarization/Allahyari2017Text.md)

    *Mehdi Allahyari, Seyedamin Pouriyeh, Mehdi Assefi, Saeid Safaei, Elizabeth D. Trippe, Juan B. Gutierrez, Krys Kochut*

#### [Tagging Chunking Syntax and Parsing](#content)

1. **A Neural Entity Coreference Resolution Review.** arXiv 2019 [paper](https://arxiv.org/abs/1910.09329) [bib](/bib/Natural-Language-Processing/Tagging,-Chunking,-Syntax-and-Parsing/Stylianou2019A.md)

    *Nikolaos Stylianou, Ioannis Vlahavas*

2. **A survey of cross-lingual features for zero-shot cross-lingual semantic parsing.** arXiv 2019 [paper](https://arxiv.org/abs/1908.10461) [bib](/bib/Natural-Language-Processing/Tagging,-Chunking,-Syntax-and-Parsing/Yang2019A.md)

    *Jingfeng Yang, Federico Fancellu, Bonnie L. Webber*

3. **A Survey on Semantic Parsing.** AKBC 2019 [paper](https://arxiv.org/abs/1812.00978) [bib](/bib/Natural-Language-Processing/Tagging,-Chunking,-Syntax-and-Parsing/Kamath2019A.md)

    *Aishwarya Kamath, Rajarshi Das*

4. **The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers.** IEEE 2018 [paper](https://arxiv.org/abs/1808.07290) [bib](/bib/Natural-Language-Processing/Tagging,-Chunking,-Syntax-and-Parsing/Zhang2018The.md)

    *Dongxiang Zhang, Lei Wang, Nuo Xu, Bing Tian Dai, Heng Tao Shen*

#### [Text Classification](#content)

1. **A Survey of Active Learning for Text Classification using Deep Neural Networks.** arXiv 2020 [paper](https://arxiv.org/abs/2008.07267) [bib](/bib/Natural-Language-Processing/Text-Classification/Schroder2020A.md)

    *Christopher Schroder, Andreas Niekler*

2. **A Survey of Naïve Bayes Machine Learning approach in Text Document Classification.** International Journal of Computer ence and Information Security 2010 [paper](https://arxiv.org/abs/1003.1795) [bib](/bib/Natural-Language-Processing/Text-Classification/Vidhya2010A.md)

    *K. A. Vidhya, G. Aghila*

3. **A survey on phrase structure learning methods for text classification.** International Journal on Natural Language Computing 2014 [paper](https://arxiv.org/abs/1406.5598) [bib](/bib/Natural-Language-Processing/Text-Classification/Prasad2014A.md)

    *Reshma Prasad, Mary Priya Sebastian*

4. **A Survey on Text Classification: From Shallow to Deep Learning.** arXiv 2020 [paper](https://arxiv.org/pdf/2008.00364.pdf) [bib](/bib/Natural-Language-Processing/Text-Classification/Li2020A.md)

    *Qian Li, Hao Peng, Jianxin Li, Congyin Xia, Renyu Yang*

5. **Deep Learning Based Text Classification: A Comprehensive Review.** arXiv 2020 [paper](https://arxiv.org/abs/2004.03705) [bib](/bib/Natural-Language-Processing/Text-Classification/Minaee2020Deep.md)

    *Shervin Minaee, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad, Meysam Chenaghlu, Jianfeng Gao*

6. **Text Classification Algorithms: A Survey.** Information 2019 [paper](https://arxiv.org/abs/1904.08067) [bib](/bib/Natural-Language-Processing/Text-Classification/Kowsari2019Text.md)

    *Kamran Kowsari, Kiana Jafari Meimandi, Mojtaba Heidarysafa, Sanjana Mendu, Laura E. Barnes, Donald E. Brown*

    
## The ML Paper List

#### [Architectures](#content)

1. **A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects.** arXiv 2020 [paper](https://arxiv.org/abs/2004.02806) [bib](/bib/Machine-Learning/Architectures/Li2020A.md)

    *Zewen Li, Wenjie Yang, Shouheng Peng, Fan Liu*

2. **A Survey of End-to-End Driving: Architectures and Training Methods.** arXiv 2020 [paper](https://arxiv.org/abs/2003.06404) [bib](/bib/Machine-Learning/Architectures/Tampuu2020A.md)

    *Ardi Tampuu, Maksym Semikin, Naveed Muhammad, Dmytro Fishman, Tambet Matiisen*

3. **A Survey on Latent Tree Models and Applications.** Journal of Artificial Intelligence Research 2013 [paper](https://arxiv.org/abs/1402.0577) [bib](/bib/Machine-Learning/Architectures/Mourad2013A.md)

    *Raphaël Mourad, Christine Sinoquet, Nevin L. Zhang, Tengfei Liu, Philippe Leray*

4. **An Attentive Survey of Attention Models.** IJCAI 2019 [paper](https://arxiv.org/abs/1904.02874) [bib](/bib/Machine-Learning/Architectures/Chaudhari2019An.md)

    *Sneha Chaudhari, Gungor Polatkan, Rohan Ramanath, Varun Mithal*

5. **Binary Neural Networks: A Survey.** Pattern Recognition 2020 [paper](https://arxiv.org/abs/2004.03333) [abib](/bib/Machine-Learning/Architectures/Qin2020Binary.md)

    *Haotong Qin, Ruihao Gong, Xianglong Liu, Xiao Bai, Jingkuan Song, Nicu Sebe*

6. **Deep Echo State Network (DeepESN): A Brief Survey.** arXiv 2017 [paper](https://arxiv.org/abs/1712.04323) [bib](/bib/Machine-Learning/Architectures/Gallicchio2017Deep.md)

    *Claudio Gallicchio, Alessio Micheli*

7. **Efficient Transformers: A Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2009.06732) [bib](/bib/Machine-Learning/Architectures/Tay2020Efficient.md)

    *Yi Tay, Mostafa Dehghani, Dara Bahri, Donald Metzler*

8. **Recent Advances in Convolutional Neural Networks.** Computer ence 2018 [paper](https://arxiv.org/abs/1512.07108v3) [bib](/bib/Machine-Learning/Architectures/Gu2018Recent.md)

    *Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Ting Liu, Xingxing Wang, Gang Wang, Jianfei Cai, Tsuhan Chen*

9. **Sum-product networks: A survey.** IEEE 2020 [paper](https://arxiv.org/abs/2004.01167) [bib](/bib/Machine-Learning/Architectures/Paris2020Sum-product.md)

    *Iago Paris, Raquel Sanchez-Cauce, Francisco Javier Díez*

10. **Survey on the attention based RNN model and its applications in computer vision.** arXiv 2016 [paper](https://arxiv.org/abs/1601.06823) [bib](/bib/Machine-Learning/Architectures/Wang2016Survey.md)

    *Feng Wang, David M. J. Tax*

11. **Understanding LSTM -- a tutorial into Long Short-Term Memory Recurrent Neural Networks.** arXiv 2019 [paper](https://arxiv.org/abs/1909.09586) [bib](/bib/Machine-Learning/Architectures/Staudemeyer2019Understanding.md)

    *Ralf C. Staudemeyer, Eric Rothstein Morris*

#### [AutoML](#content)

1. **A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions.** arXiv 2020 [paper](https://arxiv.org/abs/2006.02903) [bib](/bib/Machine-Learning/AutoML/Ren2020A.md)

    *Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Xiaojiang Chen, Xin Wang*

2. **A Survey on Neural Architecture Search.** arXiv 2019 [paper](https://arxiv.org/abs/1905.01392) [bib](/bib/Machine-Learning/AutoML/Wistuba2019A.md)

    *Martin Wistuba, Ambrish Rawat, Tejaswini Pedapati*

3. **AutoML: A Survey of the State-of-the-Art.** Knowledge Based Systems 2019 [paper](https://arxiv.org/abs/1908.00709) [bib](/bib/Machine-Learning/AutoML/He2019AutoML.md)

    *Xin He, Kaiyong Zhao, Xiaowen Chu*

4. **Benchmark and Survey of Automated Machine Learning Frameworks.** Journal of Artificial Intelligence Research 2020 [paper](https://arxiv.org/abs/1904.12054) [bib](/bib/Machine-Learning/AutoML/Zoller2020Benchmark.md)

    *Marc-Andre Zoller, Marco F. Huber*

5. **Neural Architecture Search: A Survey.** Journal of Machine Learning Research 2019 [paper](https://arxiv.org/abs/1808.05377) [bib](/bib/Machine-Learning/AutoML/Elsken2019Neural.md)

    *Thomas Elsken, Jan Hendrik Metzen, Frank Hutter*

#### [Bayesian Methods](#content)

1. **A survey of non-exchangeable priors for Bayesian nonparametric models.** IEEE 2015 [paper](https://arxiv.org/abs/1211.4798) [bib](/bib/Machine-Learning/Bayesian-Methods/Foti2015A.md)

    *Nicholas J. Foti, Sinead Williamson*

2. **A Survey on Bayesian Deep Learning.**  2020 [paper](http://arxiv.org/abs/1604.01662v3) [bib](/bib/Machine-Learning/Bayesian-Methods/Wang2016A.md)

    *Hao Wang, Dit-Yan Yeung*

3. **Bayesian Neural Networks: An Introduction and Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2006.12024) [bib](/bib/Machine-Learning/Bayesian-Methods/Ethan2020Bayesian.md)

    *Ethan Goan, Clinton Fookes*

4. **Bayesian Nonparametric Space Partitions: A Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2002.11394) [bib](/bib/Machine-Learning/Bayesian-Methods/Fan2020Bayesian.md)

    *Xuhui Fan, Bin Li, Ling Luo, Scott A. Sisson*

5. **Towards Bayesian Deep Learning: A Survey.** arXiv 2016 [paper](https://arxiv.org/abs/1604.01662) [bib](/bib/Machine-Learning/Bayesian-Methods/Wang2016Towards.md)

    *Hao Wang, Dityan Yeung*

#### [Classification Clustering and Regression](#content)

1. **A Survey of Classification Techniques in the Area of Big Data.** arXiv 2015 [paper](https://arxiv.org/abs/1503.07477) [bib](/bib/Machine-Learning/Classification,Clustering,Regression/Koturwar2015A.md)

    *Praful Koturwar, Sheetal Girase, Debajyoti Mukhopadhyay*

2. **A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges.** arXiv 2020 [paper](https://arxiv.org/abs/2006.09319) [bib](/bib/Machine-Learning/Classification,Clustering,Regression/Swiler2020A.md)

    *Laura P. Swiler, Mamikon Gulian, Ari Frankel, Cosmin Safta, John D. Jakeman*

3. **A Survey on Multi-View Clustering.** arXiv 2017 [paper](https://arxiv.org/abs/1712.06246) [bib](/bib/Machine-Learning/Classification,Clustering,Regression/Chao2017A.md)

    *Guoqing Chao, Shiliang Sun, Jinbo Bi*

4. **Deep learning for time series classification: a review.** Data Mining & Knowledge Discovery 2019 [paper](https://arxiv.org/abs/1809.04356) [bib](/bib/Machine-Learning/Classification,Clustering,Regression/Fawaz2019Deep.md)

    *Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, Pierre-Alain Muller*

5. **How Complex is your classification problem? A survey on measuring classification complexity.** ACM 2019 [paper](https://arxiv.org/abs/1808.03591) [bib](/bib/Machine-Learning/Classification,Clustering,Regression/Lorena2019How.md)

    *Ana Carolina Lorena, Luis P F Garcia, Jens Lehmann, Marcilio C P Souto, Tin K Ho*

#### [Curriculum Learning](#content)

1. **Automatic Curriculum Learning For Deep RL: A Short Survey.** IJCAI 2020 [paper](https://arxiv.org/abs/2003.04664) [bib](/bib/Machine-Learning/Curriculum-Learning/Portelas2020Automatic.md)

    *Remy Portelas, Cedric Colas, Lilian Weng, Katja Hofmann, Pierre-Yves Oudeyer*

2. **Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2003.04960) [bib](/bib/Machine-Learning/Curriculum-Learning/Narvekar2020Curriculum.md)

    *Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E. Taylor, Peter Stone*

#### [Data Augmentation](#content)

1. **A survey on Image Data Augmentation for Deep Learning.** Journal of Big Data 2019 [paper](https://link.springer.com/article/10.1186/s40537-019-0197-0) [bib](/bib/Machine-Learning/Data-Augmentation/Shorten2019A.md)

    *Connor Shorten, Taghi M. Khoshgoftaar*

2. **An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks.** arXiv 2020 [paper](https://arxiv.org/abs/2007.15951) [bib](/bib/Machine-Learning/Data-Augmentation/Brian2020An.md)

    *Brian Kenji Iwana, Seiichi Uchida*

3. **Time Series Data Augmentation for Deep Learning: A Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2002.12478) [bib](/bib/Machine-Learning/Data-Augmentation/Wen2020Time.md)

    *Qingsong Wen, Liang Sun, Xiaomin Song, Jingkun Gao, Xue Wang, Huan Xu*

#### [Deep Learning](#content)

1. **A State-of-the-Art Survey on Deep Learning Theory and Architectures.** mdpi 2019 [paper](https://www.mdpi.com/2079-9292/8/3/292) [bib](/bib/Machine-Learning/Deep-Learning---General-Methods/Alom2019A.md)

    *Alom, Md Zahangir and Taha, Tarek M and Yakopcic, Chris and Westberg, Stefan and Sidike, Paheding and Nasrin, Mst Shamima and Hasan, Mahmudul and Van Essen, Brian C and Awwal, Abdul AS and Asari, Vijayan K*

2. **A Survey of Deep Active Learning.** arXiv 2020 [paper](https://arxiv.org/abs/2009.00236) [bib](/bib/Machine-Learning/Deep-Learning---General-Methods/Ren2020A.md)

    *Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Xiaojiang Chen, Xin Wang*

3. **A Survey of Deep Learning for Data Caching in Edge Network.** arXiv 2020 [paper](https://arxiv.org/abs/2008.07235) [bib](/bib/Machine-Learning/Deep-Learning---General-Methods/Wang2020A.md)

    *Yantong Wang, Vasilis Friderikos*

4. **A Survey of Neuromorphic Computing and Neural Networks in Hardware.** arXiv 2017 [paper](https://arxiv.org/abs/1705.06963) [bib](/bib/Machine-Learning/Deep-Learning---General-Methods/Schuman2017A.md)

    *Catherine D. Schuman, Thomas E. Potok, Robert M. Patton, J. Douglas Birdwell, Mark E. Dean, Garrett S. Rose, James S. Plank*

5. **A Survey on Concept Factorization: From Shallow to Deep Representation Learning.** arXiv 2020 [paper](https://arxiv.org/abs/2007.15840) [bib](/bib/Machine-Learning/Deep-Learning---General-Methods/Zhao2020A.md)

    *Zhao Zhang, Yan Zhang, Li Zhang, Shuicheng Yan*

6. **A Survey on Deep Hashing Methods.** arXiv 2020 [paper](https://arxiv.org/abs/2003.03369) [bib](/bib/Machine-Learning/Deep-Learning---General-Methods/Luo2020A.md)

    *Xiao Luo, Chong Chen, Huasong Zhong, Hao Zhang, Minghua Deng, Jianqiang Huang, Xiansheng Hua*

7. **A survey on modern trainable activation functions.** arXiv 2020 [paper](https://arxiv.org/abs/2005.00817) [bib](/bib/Machine-Learning/Deep-Learning---General-Methods/Apicella2020A.md)

    *Andrea Apicella, Francesco Donnarumma, Francesco Isgrò, Roberto Prevete*

8. **Big Networks: A Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2008.03638) [bib](/bib/Machine-Learning/Deep-Learning---General-Methods/Bedru2020A.md)

    *Hayat Dino Bedru, Shuo Yu, Xinru Xiao, Da Zhang, Liangtian Wan, He Guo, Feng Xia*

9. **Convergence of Edge Computing and Deep Learning: A Comprehensive Survey.** IEEE 2020 [paper](https://ieeexplore.ieee.org/document/8976180) [bib](/bib/Machine-Learning/Deep-Learning---General-Methods/Wang2020Convergence.md)

    *Xiaofei Wang, Yiwen Han, Victor C.M. Leung, Dusit Niyato, Xueqiang Yan, Xu Chen*

10. **Deep learning.** Nature 2015 [paper](https://www.nature.com/articles/nature14539) [bib](/bib/Machine-Learning/Deep-Learning---General-Methods/LeCun2015Deep.md)

    *Yann LeCun*

11. **Deep Learning for 3D Point Cloud Understanding: A Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2009.08920) [bib](/bib/Machine-Learning/Deep-Learning---General-Methods/Lu2020Deep.md)

    *Haoming Lu, Humphrey Shi*

12. **Deep Learning for Image Super-resolution: A Survey.** IEEE 2019 [paper](https://arxiv.org/abs/1902.06068) [bib](/bib/Machine-Learning/Deep-Learning---General-Methods/Wang2019Deep.md)

    *Zhihao Wang, Jian Chen, Steven C.H. Hoi*

13. **Deep Learning on Graphs: A Survey.** IEEE 2020 [paper](https://arxiv.org/abs/1812.04202) [bib](/bib/Machine-Learning/Deep-Learning---General-Methods/Zhang2020Deep.md)

    *Ziwei Zhang, Peng Cui, Wenwu Zhu*

14. **Deep Learning Theory Review: An Optimal Control and Dynamical Systems Perspective.** arXiv 2019 [paper](https://arxiv.org/abs/1908.10920) [bib](/bib/Machine-Learning/Deep-Learning---General-Methods/Liu2019Deep.md)

    *Guan-Horng Liu, Evangelos A. Theodorou*

15. **Geometric Deep Learning: Going beyond Euclidean data.** IEEE 2017 [paper](https://arxiv.org/abs/1611.08097) [bib](/bib/Machine-Learning/Deep-Learning---General-Methods/Bronstein2017Geometric.md)

    *Michael M. Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, Pierre Vandergheynst*

16. **Hands-on Bayesian Neural Networks - a Tutorial for DeepLearning Users.** arXiv 2020 [paper](https://arxiv.org/abs/2007.06823) [bib](/bib/Machine-Learning/Deep-Learning---General-Methods/Laurent2020Hands.md)

    *Laurent Valentin Jospin, et al*

17. **Improving Deep Learning Models via Constraint-Based Domain Knowledge: a Brief Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2005.10691) [bib](/bib/Machine-Learning/Deep-Learning---General-Methods/Borghesi2020Improving.md)

    *Andrea Borghesi, Federico Baldo, Michela Milano*

18. **Learning from Noisy Labels with Deep Neural Networks: A Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2007.08199) [bib](/bib/Machine-Learning/Deep-Learning---General-Methods/Song2020Learning.md)

    *Hwanjun Song, Minseok Kim, Dongmin Park, Jae-Gil Lee*

19. **Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2007.00753) [bib](/bib/Machine-Learning/Deep-Learning---General-Methods/Samuel2020Opportunities.md)

    *Samuel Henrique Silva, Peyman Najafirad*

20. **Pooling Methods in Deep Neural Networks, a Review.** arXiv 2020 [paper](https://arxiv.org/abs/2009.07485) [bib](/bib/Machine-Learning/Deep-Learning---General-Methods/Gholamalinezhad2020Pooling.md)

    *Hossein Gholamalinezhad, Hossein Khosravi*

21. **Privacy in Deep Learning: A Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2004.12254) [bib](/bib/Machine-Learning/Deep-Learning---General-Methods/Fatemehsadat2020Privacy.md)

    *Fatemehsadat Mireshghallah, Mohammadkazem Taram, Praneeth Vepakomma, Abhishek Singh, Ramesh Raskar, Hadi Esmaeilzadeh*

22. **Review: Ordinary Differential Equations For Deep Learning.** arXiv 2019 [paper](https://arxiv.org/abs/1911.00502) [bib](/bib/Machine-Learning/Deep-Learning---General-Methods/Chen2019Review.md)

    *Xinshi Chen*

23. **Short-term Traffic Prediction with Deep Neural Networks: A Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2009.00712) [bib](/bib/Machine-Learning/Deep-Learning---General-Methods/Lee2020Short.md)

    *Kyungeun Lee, Moonjung Eo, Euna Jung, Yoonjin Yoon, Wonjong Rhee*

24. **Survey of Dropout Methods for Deep Neural Networks.** arXiv 2019 [paper](https://arxiv.org/abs/1904.13310) [bib](/bib/Machine-Learning/Deep-Learning---General-Methods/Labach2019Survey.md)

    *Alex Labach, Hojjat Salehinejad, Shahrokh Valaee*

25. **Survey of Expressivity in Deep Neural Networks.** NIPS 2016 [paper](https://arxiv.org/abs/1611.08083) [bib](/bib/Machine-Learning/Deep-Learning---General-Methods/Raghu2016Survey.md)

    *Maithra Raghu, Ben Poole, Jon Kleinberg, Surya Ganguli, Jascha Sohldickstein*

26. **Survey of reasoning using Neural networks.** arXiv 2017 [paper](https://arxiv.org/abs/1702.06186) [bib](/bib/Machine-Learning/Deep-Learning---General-Methods/Sahu2017Survey.md)

    *Amit Sahu*

27. **The Deep Learning Compiler: A Comprehensive Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2002.03794) [bib](/bib/Machine-Learning/Deep-Learning---General-Methods/Li2020The.md)

    *Mingzhen Li, Yi Liu, Xiaoyan Liu, Qingxiao Sun, Xin You, Hailong Yang, Zhongzhi Luan, Depei Qian*

28. **The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches.** arXiv 2018 [paper](https://arxiv.org/abs/1803.01164) [bib](/bib/Machine-Learning/Deep-Learning---General-Methods/Alom2018The.md)

    *Zahangir Alom, Tarek M Taha, Christopher Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Brian C Van Esesn, Abdul A S Awwal, Vijayan K Asari*

29. **Time Series Forecasting With Deep Learning: A Survey.** Philosophical Transactions of the Royal Society 2020 [paper](https://arxiv.org/abs/2004.13408) [bib](/bib/Machine-Learning/Deep-Learning---General-Methods/Lim2020Time.md)

    *Bryan Lim, Stefan Zohren*

#### [Deep Reinforcement Learning](#content)

1. **A Brief Survey of Deep Reinforcement Learning.** IEEE 2017 [paper](https://arxiv.org/abs/1708.05866) [bib](/bib/Machine-Learning/Deep-Reinforcement-Learning/Arulkumaran2017A.md)

    *Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil A Bharath*

2. **A Short Survey On Memory Based Reinforcement Learning.** arXiv 2019 [paper](https://arxiv.org/abs/1904.06736v1) [bib](/bib/Machine-Learning/Deep-Reinforcement-Learning/Ramani2019A.md)

    *Dhruv Ramani*

3. **A Short Survey on Probabilistic Reinforcement Learning.** arXiv 2019 [paper](https://arxiv.org/abs/1901.07010) [bib](/bib/Machine-Learning/Deep-Reinforcement-Learning/Russel2019A.md)

    *Reazul Hasan Russel*

4. **A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress.** arXiv 2018 [paper](https://arxiv.org/abs/1806.06877) [bib](/bib/Machine-Learning/Deep-Reinforcement-Learning/Arora2018A.md)

    *Saurabh Arora, Prashant Doshi*

5. **A Survey of Reinforcement Learning Algorithms for Dynamically Varying Environments.** arXiv 2020 [paper](https://arxiv.org/abs/2005.10619) [bib](/bib/Machine-Learning/Deep-Reinforcement-Learning/Padakandla2020A.md)

    *Sindhu Padakandla*

6. **A Survey of Reinforcement Learning Informed by Natural Language.** IJCAI 2019 [paper](https://arxiv.org/abs/1906.03926) [bib](/bib/Machine-Learning/Deep-Reinforcement-Learning/Luketina2019A.md)

    *Jelena Luketina, Nantas Nardelli, Gregory Farquhar, Jakob N. Foerster, Jacob Andreas, Edward Grefenstette, Shimon Whiteson, Tim Rocktäschel*

7. **A Survey of Reinforcement Learning Techniques: Strategies, Recent Development, and Future Directions.** arXiv 2020 [paper](https://arxiv.org/abs/2001.06921) [bib](/bib/Machine-Learning/Deep-Reinforcement-Learning/Mondal2020A.md)

    *Amit Kumar Mondal*

8. **A survey on intrinsic motivation in reinforcement learning.** arXiv 2019 [paper](https://arxiv.org/abs/1908.06976) [bib](/bib/Machine-Learning/Deep-Reinforcement-Learning/Aubret2019A.md)

    *Aubret, Arthur, Matignon, Laetitia, Hassas, Salima*

9. **A Survey on Reproducibility by Evaluating Deep Reinforcement Learning Algorithms on Real-World Robots.** Conference on Robot Learning 2019 [paper](https://arxiv.org/abs/1909.03772) [bib](/bib/Machine-Learning/Deep-Reinforcement-Learning/Anton2019A.md)

    *Nicolai A. Lynnerup, Laura Nolling, Rasmus Hasle, John Hallam*

10. **Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey.** Journal of Machine Learning Research 2020 [paper](https://arxiv.org/pdf/2003.04960v2.pdf) [bib](/bib/Machine-Learning/Deep-Reinforcement-Learning/Narvekar2020Curriculum.md)

    *Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E. Taylor, Peter Stone*

11. **Deep Reinforcement Learning: An Overview.** arXiv 2017 [paper](https://arxiv.org/abs/1701.07274) [bib](/bib/Machine-Learning/Deep-Reinforcement-Learning/Li2017Deep.md)

    *Yuxi Li*

12. **Feature-Based Aggregation and Deep Reinforcement Learning: A Survey and Some New Implementations.** IEEE 2019 [paper](https://arxiv.org/abs/1804.04577) [bib](/bib/Machine-Learning/Deep-Reinforcement-Learning/Bertsekas2019Feature-based.md)

    *Dimitri P. Bertsekas*

13. **Model-Based Deep Reinforcement Learning for High-Dimensional Problems, a Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2008.05598) [bib](/bib/Machine-Learning/Deep-Reinforcement-Learning/Plaat2020Model.md)

    *Aske Plaat, Walter Kosters, Mike Preuss*

14. **Model-based Reinforcement Learning: {A} Survey.** CoRR 2020 [paper](https://arxiv.org/abs/2006.16712) [bib](/bib/Machine-Learning/Deep-Reinforcement-Learning/Thomas2020Model.md)

    *Thomas M. Moerland, Joost Broekens,  Catholijn M. Jonker*

15. **Reinforcement Learning for Combinatorial Optimization: A Survey.** arxiv 2020 [paper](http://arxiv.org/pdf/2003.03600v2.pdf) [bib](/bib/Machine-Learning/Deep-Reinforcement-Learning/Mazyavkina2020Reinforcement.md)

    *Nina Mazyavkina, Sergey Sviridov, Sergei Ivanov, Evgeny Burnaev*

16. **Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey.** IEEE 2020 [paper](https://arxiv.org/pdf/2009.13303v1.pdf) [bib](/bib/Machine-Learning/Deep-Reinforcement-Learning/Zhao2020Sim.md)

    *Wenshuai Zhao, Jorge Peña Queralta, Tomi Westerlund*

#### [Federated Learning](#content)

1. **A Survey towards Federated Semi-supervised Learning.** arXiv 2020 [paper](https://arxiv.org/abs/2002.11545v1) [bib](/bib/Machine-Learning/Federated-Learning/Jin2020A.md)

    *Yilun Jin, Xiguang Wei, Yang Liu, Qiang Yang*

2. **Advances and Open Problems in Federated Learning.** arXiv 2019 [paper](https://arxiv.org/abs/1912.04977) [bib](/bib/Machine-Learning/Federated-Learning/Kairouz2019Advances.md)

    *Peter Kairouz, H Brendan Mcmahan, Brendan Avent, Aurelien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G L Doliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary A Garrett, Adria Gascon, Badih Ghazi, Phillip B Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrede Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Ozgur, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramer, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X Yu, Han Yu, Sen Zhao*

3. **Threats to Federated Learning: A Survey.** Conference on Robot Learning 2020 [paper](https://arxiv.org/abs/2003.02133) [bib](/bib/Machine-Learning/Federated-Learning/Lyu2020Threats.md)

    *Lingjuan Lyu, Han Yu, Qiang Yang*

4. **Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective.** arXiv 2020 [paper](https://arxiv.org/abs/2002.11545v2) [bib](/bib/Machine-Learning/Federated-Learning/Jin2020Towards.md)

    *Yilun Jin, Xiguang Wei, Yang Liu, Qiang Yang*

#### [Few-Shot and Zero-Shot Learning](#content)

1. **A Survey of Zero-Shot Learning: Settings, Methods, and Applications.** ACM Transactions on Intelligent Systems and Technology 2019 [paper](https://dl.acm.org/doi/10.1145/3293318) [bib](/bib/Machine-Learning/Few-Shot-and-Zero-Shot-Learning/Wang2019A.md)

    *Wei Wang,Vincent W. Zheng,Han Yu,Chunyan Miao*

2. **Few-shot Learning: A Survey.** arXiv 2019 [paper](https://arxiv.org/abs/1904.05046v1) [bib](/bib/Machine-Learning/Few-Shot-and-Zero-Shot-Learning/Wang2019Few-shot.md)

    *Yaqing Wang, Quanming Yao*

3. **Generalizing from a Few Examples: A Survey on Few-Shot Learning.** ACM Computing Surveys 2019 [paper](https://arxiv.org/abs/1904.05046) [bib](/bib/Machine-Learning/Few-Shot-and-Zero-Shot-Learning/Wang2020Generalizing.md)

    *Yaqing Wang, Quanming Yao, James Kwok, Lionel M. Ni*

4. **Learning from Few Samples: A Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2007.15484) [bib](/bib/Machine-Learning/Few-Shot-and-Zero-Shot-Learning/bendre2020learning.md)

    *Nihar Bendre, Hugo Terashima Marín, Peyman Najafirad*

5. **Learning from Very Few Samples: A Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2009.02653) [bib](/bib/Machine-Learning/Few-Shot-and-Zero-Shot-Learning/Lu2020Learning.md)

    *Jiang Lu, Pinghua Gong, Jieping Ye, Changshui Zhang*

#### [General Machine Learning](#content)

1. **A survey of dimensionality reduction techniques.** arXiv 2014 [paper](https://arxiv.org/abs/1403.2877) [bib](/bib/Machine-Learning/General-Machine-Learning/Sorzano2014A.md)

    *C.O.S. Sorzano, J. Vargas, A. Pascual Montano*

2. **A Survey of Predictive Modelling under Imbalanced Distributions.** arXiv 2015 [paper](https://arxiv.org/abs/1505.01658) [bib](/bib/Machine-Learning/General-Machine-Learning/Branco2015A.md)

    *Paula Branco, Luis Torgo, Rita Ribeiro*

3. **A Survey on Activation Functions and their relation with Xavier and He Normal Initialization.** arXiv 2020 [paper](https://arxiv.org/abs/2004.06632) [bib](/bib/Machine-Learning/General-Machine-Learning/Datta2020A.md)

    *Leonid Datta*

4. **A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspective.** IEEE 2018 [paper](https://arxiv.org/abs/1811.03402) [bib](/bib/Machine-Learning/General-Machine-Learning/Roh2018A.md)

    *Yuji Roh, Geon Heo, Steven Euijong Whang*

5. **A survey on feature weighting based K-Means algorithms.** Journal of Classification 2016 [paper](https://arxiv.org/abs/1601.03483) [bib](/bib/Machine-Learning/General-Machine-Learning/Amorim2016A.md)

    *Renato Cordeiro de Amorim*

6. **A Survey on Graph Kernels.** Applied Network ence 2020 [paper](https://appliednetsci.springeropen.com/articles/10.1007/s41109-019-0195-3) [bib](/bib/Machine-Learning/General-Machine-Learning/Kriege2020A.md)

    *Nils M. Kriege, Fredrik D. Johansson, Christopher Morris*

7. **A Survey on Large-Scale Machine Learning.** IEEE 2020 [paper](https://arxiv.org/abs/2008.03911?context=stat.ML) [bib](/bib/Machine-Learning/General-Machine-Learning/Wang2020A.md)

    *Meng Wang，Weijie Fu，Xiangnan He，Shijie Hao，Xindong Wu*

8. **A Survey on Multi-output Learning.** IEEE 2019 [paper](https://arxiv.org/abs/1901.00248) [bib](/bib/Machine-Learning/General-Machine-Learning/Xu2019A.md)

    *Donna Xu, Yaxin Shi, Ivor W. Tsang, Yew-Soon Ong, Chen Gong, Xiaobo Shen*

9. **A Survey on Resilient Machine Learning.** arXiv 2017 [paper](https://arxiv.org/abs/1707.03184) [bib](/bib/Machine-Learning/General-Machine-Learning/Kumar2017A.md)

    *Atul Kumar, Sameep Mehta*

10. **A Survey on Surrogate Approaches to Non-negative Matrix Factorization.** Vietnam journal of mathematics 2018 [paper](https://arxiv.org/abs/1808.01975) [bib](/bib/Machine-Learning/General-Machine-Learning/Fernsel2018A.md)

    *Pascal Fernsel, Peter Maass*

11. **A Tutorial on Network Embeddings.** arXiv 2018 [paper](https://arxiv.org/abs/1808.02590) [bib](/bib/Machine-Learning/General-Machine-Learning/Chen2018A.md)

    *Haochen Chen, Bryan Perozzi, Rami Al-Rfou, Steven Skiena*

12. **Adversarial Examples in Modern Machine Learning: A Review.** arXiv 2019 [paper](https://arxiv.org/abs/1911.05268) [bib](/bib/Machine-Learning/General-Machine-Learning/Wiyatno2019Adversarial.md)

    *Rey Reza Wiyatno, Anqi Xu, Ousmane Dia, Archy de Berker*

13. **Algorithms Inspired by Nature: A Survey.** arXiv 2019 [paper](https://arxiv.org/abs/1903.01893) [bib](/bib/Machine-Learning/General-Machine-Learning/Gupta2019Algorithms.md)

    *Pranshu Gupta*

14. **Deep Tree Transductions - A Short Survey.** INNS Big Data and Deep Learning 2019 [paper](https://arxiv.org/abs/1902.01737) [bib](/bib/Machine-Learning/General-Machine-Learning/Bacciu2019Deep.md)

    *Davide Bacciu, Antonio Bruno*

15. **Graph Representation Learning: A Survey.** APSIPA Transactions on Signal and Information Processing 2020 [paper](http://jmlr.csail.mit.edu/papers/v21/19-447.html) [bib](/bib/Machine-Learning/General-Machine-Learning/Chen2020Graph.md)

    *Fenxiao Chen, Yuncheng Wang, Bin Wang, C.-C. Jay Kuo*

16. **Heuristic design of fuzzy inference systems: A review of three decades of research.** Engineering Applications of Artificial Intelligence 2019 [paper](https://arxiv.org/abs/1908.10122) [bib](/bib/Machine-Learning/General-Machine-Learning/Ojha2019Heuristic.md)

    *Varun Ojha, Ajith Abraham, Vaclav Snasel*

17. **Hierarchical Mixtures-of-Experts for Exponential Family Regression Models with Generalized Linear Mean Functions: A Survey of Approximation and Consistency Results.** Uncertainty in Artificial Intelligence 2013 [paper](https://arxiv.org/abs/1301.7390) [bib](/bib/Machine-Learning/General-Machine-Learning/Jiang2013Hierarchical.md)

    *Wenxin Jiang, Martin A. Tanner*

18. **Hyperbox based machine learning algorithms: A comprehensive survey.** arXiv 2019 [paper](https://arxiv.org/abs/1901.11303) [bib](/bib/Machine-Learning/General-Machine-Learning/Khuat2019Hyperbox.md)

    *Thanh Tung Khuat, Dymitr Ruta, Bogdan Gabrys*

19. **Imbalance Problems in Object Detection: A Review.** IEEE 2020 [paper](https://arxiv.org/abs/1909.00169) [bib](/bib/Machine-Learning/General-Machine-Learning/Oksuz2020Imbalance.md)

    *Kemal Oksuz, Baris Can Cam, Sinan Kalkan, Emre Akbas*

20. **Learning Representations of Graph Data -- A Survey.** arXiv 2019 [paper](https://arxiv.org/abs/1906.02989) [bib](/bib/Machine-Learning/General-Machine-Learning/Kinderkhedia2019Learning.md)

    *Mital Kinderkhedia*

21. **Machine Learning at the Network Edge: A Survey.** arXiv 2019 [paper](https://arxiv.org/abs/1908.00080) [bib](/bib/Machine-Learning/General-Machine-Learning/Murshed2019Machine.md)

    *M.G. Sarwar Murshed, Christopher Murphy, Daqing Hou, Nazar Khan, Ganesh Ananthanarayanan, Faraz Hussain*

22. **Machine Learning for Spatiotemporal Sequence Forecasting: A Survey.** arXiv 2018 [paper](https://arxiv.org/abs/1808.06865) [bib](/bib/Machine-Learning/General-Machine-Learning/Shi2018Machine.md)

    *Xingjian Shi, Dit-Yan Yeung*

23. **Machine Learning in Network Centrality Measures: Tutorial and Outlook.** ACM Computing Surveys 2019 [paper](https://dl.acm.org/doi/10.1145/3237192) [bib](/bib/Machine-Learning/General-Machine-Learning/Grando2019Machine.md)

    *Felipe Grando, Lisandro Zambenedetti Granville, Luís C. Lamb*

24. **Machine Learning Testing: Survey, Landscapes and Horizons.** IEEE 2019 [paper](https://arxiv.org/abs/1906.10742v1) [bib](/bib/Machine-Learning/General-Machine-Learning/Zhang2019Machine.md)

    *Jie M. Zhang, Mark Harman, Lei Ma, Yang Liu*

25. **Machine Learning with World Knowledge: The Position and Survey.** arXiv 2017 [paper](https://arxiv.org/abs/1705.02908) [bib](/bib/Machine-Learning/General-Machine-Learning/Song2017Machine.md)

    *Yangqiu Song, Dan Roth*

26. **Mean-Field Learning: a Survey.** arXiv 2012 [paper](https://arxiv.org/abs/1210.4657) [bib](/bib/Machine-Learning/General-Machine-Learning/Hamidou2012Mean-Field.md)

    *Hamidou Tembine, Raúl Tempone, Pedro Vilanova*

27. **Multidimensional Scaling, Sammon Mapping, and Isomap: Tutorial and Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2009.08136) [bib](/bib/Machine-Learning/General-Machine-Learning/Ghojogh2020Multidimensional.md)

    *Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley*

28. **Multimodal Machine Learning: A Survey and Taxonomy.** arXiv 2017 [paper](https://arxiv.org/abs/1705.09406) [bib](/bib/Machine-Learning/General-Machine-Learning/Baltrusaitis2017Multimodal.md)

    *Tadas Baltrusaitis, Chaitanya Ahuja, Louis-Philippe Morency*

29. **Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey.** Autonomous Agents and Multi Agent Systems 2020 [paper](https://link.springer.com/content/pdf/10.1007/s10458-019-09433-x.pdf) [bib](/bib/Machine-Learning/General-Machine-Learning/Radulescu2020Multi-objective.md)

    *Roxana Rădulescu, Patrick Mannion, Diederik M. Roijers, Ann Nowé*

30. **Narrative Science Systems: A Review.** Computer ence 2015 [paper](https://arxiv.org/abs/1510.04420) [bib](/bib/Machine-Learning/General-Machine-Learning/Sarao2015Narrative.md)

    *Paramjot Kaur Sarao, Puneet Mittal, Rupinder Kaur*

31. **Network Representation Learning: A Survey.** IEEE 2020 [paper](https://www.computer.org/csdl/journal/bd/2020/01/08395024/1hN4aUycB8Y) [bib](/bib/Machine-Learning/General-Machine-Learning/Zhang2020Network.md)

    *Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang*

32. **Relational inductive biases, deep learning, and graph networks.** arXiv 2018 [paper](http://arxiv.org/abs/1806.01261) [bib](/bib/Machine-Learning/General-Machine-Learning/Battaglia2018Relational.md)

    *Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Flores Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Caglar Gülçehre, H. Francis Song, Andrew J. Ballard, Justin Gilmer, George E. Dahl, Ashish Vaswani, Kelsey R. Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matthew Botvinick, Oriol Vinyals, Yujia Li, Razvan Pascanu*

33. **Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey.** JMLR 2019 [paper](https://arxiv.org/abs/1905.11485) [bib](/bib/Machine-Learning/General-Machine-Learning/Kazemi2019Relational.md)

    *Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain, Ivan Kobyzev, Akshay Sethi, Peter Forsyth, Pascal Poupart*

34. **Self-supervised Learning: Generative or Contrastive.** arXiv 2020 [paper](https://arxiv.org/abs/2006.08218) [bib](/bib/Machine-Learning/General-Machine-Learning/Liu2020Self.md)

    *Xiao Liu, Fanjin Zhang, Zhenyu Hou, Zhaoyu Wang, Li Mian, Jing Zhang, Jie Tang*

35. **Statistical Queries and Statistical Algorithms: Foundations and Applications.** arXiv 2020 [paper](https://arxiv.org/abs/2004.00557) [bib](/bib/Machine-Learning/General-Machine-Learning/Reyzin2020Statistical.md)

    *Lev Reyzin*

36. **Structure Learning of Probabilistic Graphical Models: A Comprehensive Survey.** Eprint Arxiv 2011 [paper](https://arxiv.org/abs/1111.6925) [bib](/bib/Machine-Learning/General-Machine-Learning/Zhou2011Structure.md)

    *Yang Zhou*

37. **Survey on Feature Selection.** Computer ence 2015 [paper](https://arxiv.org/abs/1510.02892) [bib](/bib/Machine-Learning/General-Machine-Learning/Abdallah2015Survey.md)

    *Tarek Amr Abdallah, Beatriz de La Iglesia*

38. **Survey on Five Tribes of Machine Learning and the Main Algorithms.** Software Guide 2019 [paper](http://en.cnki.com.cn/Article_en/CJFDTotal-RJDK201907003.htm) [bib](/bib/Machine-Learning/General-Machine-Learning/Li2019Survey.md)

    *LI Xu-ran, DING Xiao-hong*

39. **Survey: Machine Learning in Production Rendering.** arXiv 2020 [paper](https://arxiv.org/abs/2005.12518v1) [bib](/bib/Machine-Learning/General-Machine-Learning/Zhu2020Survey.md)

    *Shilin Zhu*

40. **The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime Analyses.** Theory of Evolutionary Computation 2018 [paper](https://arxiv.org/abs/1801.10087) [bib](/bib/Machine-Learning/General-Machine-Learning/Sudholt2018The.md)

    *Dirk Sudholt*

41. **Tutorial on Variational Autoencoders.** arXiv 2016 [paper](https://arxiv.org/pdf/1606.05908.pdf) [bib](/bib/Machine-Learning/General-Machine-Learning/Doersch2016Tutorial.md)

    *Carl Doersch*

42. **Unsupervised Cross-Lingual Representation Learning.** ACL 2019 [paper](https://www.aclweb.org/anthology/P19-4007.pdf) [bib](/bib/Machine-Learning/General-Machine-Learning/Ruder2019Preslav.md)

    *Sebastian Ruder, Anders Søgaard, Ivan Vulic*

43. **Verification for Machine Learning, Autonomy, and Neural Networks Survey.** arXiv 2018 [paper](https://arxiv.org/abs/1810.01989) [bib](/bib/Machine-Learning/General-Machine-Learning/Xiang2018Verification.md)

    *Weiming Xiang, Patrick Musau, Ayana A. Wild, Diego Manzanas Lopez, Nathaniel Hamilton, Xiaodong Yang, Joel Rosenfeld, Taylor T. Johnson*

#### [Generative Adversarial Networks](#content)

1. **A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications.** arXiv 2020 [paper](https://arxiv.org/abs/2001.06937) [bib](/bib/Machine-Learning/Generative-Adversarial-Networks/Gui2020A.md)

    *Jie Gui, Zhenan Sun, Yonggang Wen, Dacheng Tao, Jieping Ye*

2. **A Survey on Generative Adversarial Networks: Variants, Applications, and Training.** arXiv 2020 [paper](https://arxiv.org/abs/2006.05132) [bib](/bib/Machine-Learning/Generative-Adversarial-Networks/Jabbar2020A.md)

    *Abdul Jabbar, Xi Li, Bourahla Omar*

3. **Adversarial Examples on Object Recognition: A Comprehensive Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2008.03911) [bib](/bib/Machine-Learning/Generative-Adversarial-Networks/Serban2020Adversarial.md)

    *Alex Serban, Erik Poll, Joost Visser*

4. **Generative Adversarial Networks: A Survey and Taxonomy.** arXiv 2019 [paper](https://arxiv.org/abs/1906.01529) [bib](/bib/Machine-Learning/Generative-Adversarial-Networks/Wang2019Generative.md)

    *Zhengwei Wang, Qi She, Tomas E Ward*

5. **Generative Adversarial Networks: An Overview.** IEEE 2018 [paper](https://arxiv.org/abs/1710.07035) [bib](/bib/Machine-Learning/Generative-Adversarial-Networks/Creswell2018Generative.md)

    *Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, Anil A Bharath*

6. **How Generative Adversarial Nets and its variants Work: An Overview of GAN.** arXiv 2017 [paper](https://arxiv.org/abs/1711.05914v6) [bib](/bib/Machine-Learning/Generative-Adversarial-Networks/Hong2017How.md)

    *Yongjun Hong, Uiwon Hwang, Jaeyoon Yoo, Sungroh Yoon*

7. **Stabilizing Generative Adversarial Network Training: A Survey.** arXiv 2020 [paper](https://arxiv.org/abs/1910.00927) [bib](/bib/Machine-Learning/Generative-Adversarial-Networks/Wiatrak2019Stabilizing.md)

    *Maciej Wiatrak, Stefano V. Albrecht, Andrew Nystrom*

#### [Graph Neural Networks](#content)

1. **A Comprehensive Survey on Graph Neural Networks.** IEEE 2019 [paper](https://arxiv.org/abs/1901.00596) [bib](/bib/Machine-Learning/Graph-Neural-Networks/Wu2019A.md)

    *Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu*

2. **A Survey on The Expressive Power of Graph Neural Networks.** arXiv 2020 [paper](https://arxiv.org/abs/2003.04078) [bib](/bib/Machine-Learning/Graph-Neural-Networks/Sato2020A.md)

    *Ryoma Sato*

3. **Adversarial Attack and Defense on Graph Data: A Survey.** arXiv 2018 [paper](https://arxiv.org/abs/1812.10528) [bib](/bib/Machine-Learning/Graph-Neural-Networks/Sun2018Adversarial.md)

    *Lichao Sun, Ji Wang, Philip S. Yu, Bo Li*

4. **Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks.** arXiv 2020 [paper](https://arxiv.org/abs/2002.11867) [bib](/bib/Machine-Learning/Graph-Neural-Networks/Chen2020Bridging.md)

    *Zhiqian Chen, Fanglan Chen, Lei Zhang, Taoran Ji, Kaiqun Fu, Liang Zhao, Feng Chen, Chang-Tien Lu*

5. **Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey.** arXiv 2020 [paper](https://arxiv.org/abs/2005.07496) [bib](/bib/Machine-Learning/Graph-Neural-Networks/Skarding2020Foundations.md)

    *Joakim Skarding, Bogdan Gabrys, Katarzyna Musial*

6. **Graph embedding techniques, applications, and performance: A survey.** Knowledge Based Systems 2018 [paper](https://arxiv.org/abs/1705.02801) [bib](/bib/Machine-Learning/Graph-Neural-Networks/Goyal2018Graph.md)

    *Palash Goyal, Emilio Ferrara*

7. **Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective.** arXiv 2020 [paper](https://arxiv.org/abs/2003.00330) [bib](/bib/Machine-Learning/Graph-Neural-Networks/Lamb2020Graph.md)

    *Luis C. Lamb, Artur Garcez, Marco Gori, Marcelo Prates, Pedro Avelar, Moshe Vardi*

8. **Graph Neural Networks: A Review of Methods and Applications.** arXiv 2018 [paper](https://arxiv.org/abs/1812.08434) [bib](/bib/Machine-Learning/Graph-Neural-Networks/Zhou2018Graph.md)

    *Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Maosong Sun*

9. **Introduction to Graph Neural Networks.** IEEE 2020 [paper](https://ieeexplore.ieee.org/document/9048171) [bib](/bib/Machine-Learning/Graph-Neural-Networks/Liu2020Introduction.md)

    *Zhiyuan Liu, Jie Zhou*

10. **Tackling Graphical NLP problems with Graph Recurrent Networks.** arXiv 2019 [paper](https://arxiv.org/abs/1907.06142) [bib](/bib/Machine-Learning/Graph-Neural-Networks/Song2019Tackling.md)

    *Linfeng Song*

#### [Interpretability and Analysis](#content)

1. **A Survey Of Methods For Explaining Black Box Models.** ACM Computing Surveys 2018 [paper](https://dl.acm.org/doi/10.1145/3236009) [bib](/bib/Machine-Learning/Interpretability-and-Analysis/Guidotti2019A.md)

    *Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, Dino Pedreschi*

2. **A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability.** Computer ence 2018 [paper](https://arxiv.org/abs/1812.08342) [bib](/bib/Machine-Learning/Interpretability-and-Analysis/Huang2018Survey.md)

    *Xiaowei Huang, Daniel Kroening, Wenjie Ruan, James Sharp, Youcheng Sun, Emese Thamo, Min Wu, Xinping Yi*

3. **Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation.** Sigkdd Explorations 2020 [paper](https://arxiv.org/abs/2003.03934) [bib](/bib/Machine-Learning/Interpretability-and-Analysis/Moraffah2020Causal.md)

    *Raha Moraffah, Mansooreh Karami, Ruocheng Guo, Adrienne Raglin, Huan Liu*

4. **Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI.** Information Fusion 2020 [paper](https://arxiv.org/abs/1910.10045) [bib](/bib/Machine-Learning/Interpretability-and-Analysis/Arrieta2020Explainable.md)

    *Alejandro Barredo Arrieta, Natalia Diazrodriguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador Garcia, Sergio Gillopez, Daniel Molina, Richard Benjamins, Raja Chatila, Francisco Herrera*

5. **Explainable Reinforcement Learning: A Survey.** CD-MAKE 2020 2020 [paper](https://arxiv.org/abs/2005.06247) [bib](/bib/Machine-Learning/Interpretability-and-Analysis/Puiutta2020Explainable.md)

    *Erika Puiutta, Eric M. S. P. Veith*

6. **Foundations of Explainable Knowledge-Enabled Systems.** Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges/arXiv 2020 [paper](https://arxiv.org/abs/2003.07520) [bib](/bib/Machine-Learning/Interpretability-and-Analysis/Chari2020Foundations.md)

    *Shruthi Chari*

7. **How Generative Adversarial Networks and Their Variants Work: An Overview.** IEEE 2017 [paper](https://arxiv.org/abs/1711.05914) [bib](/bib/Machine-Learning/Interpretability-and-Analysis/Hong2019How.md)

    *Yongjun Hong, Uiwon Hwang, Jaeyoon Yoo, Sungroh Yoon*

8. **Language (Technology) is Power: A Critical Survey of "Bias" in NLP.** Association for Computational Linguistics 2020 [paper](https://arxiv.org/abs/2005.14050) [bib](/bib/Machine-Learning/Interpretability-and-Analysis/Blodgett2020Language.md)

    *Su Lin Blodgett, Solon Barocas, Hal Daumé III, Hanna Wallach*

9. **Opportunities and Challenges in Explainable Artificial Intelligence(XAI): A Survey.** CoRR 2020 [paper](https://arxiv.org/abs/2006.11371) [bib](/bib/Machine-Learning/Interpretability-and-Analysis/Arun2020Opportunities.md)

    *Arun Das, Paul Rad*

10. **Survey & Experiment: Towards the Learning Accuracy.** arXiv 2010 [paper](https://arxiv.org/abs/1012.4051) [bib](/bib/Machine-Learning/Interpretability-and-Analysis/Zhu2010Survey.md)

    *Zeyuan Allen Zhu*

11. **Survey of explainable machine learning with visual and granular methods beyond quasi-explanations.** arXiv 2020 [paper](https://arxiv.org/abs/2009.10221) [bib](/bib/Machine-Learning/Interpretability-and-Analysis/Kovalerchuk2020Survey.md)

    *Kovalerchuk, Boris and Ahmad, Muhammad Aurangzeb and Teredesai, Ankur*

12. **Understanding Neural Networks via Feature Visualization: A survey.** arXiv 2019 [paper](https://arxiv.org/abs/1904.08939) [bib](/bib/Machine-Learning/Interpretability-and-Analysis/Nguyen2019Understanding.md)

    *Anh Nguyen, Jason Yosinski, Jeff Clune*

13. **Visual interpretability for deep learning: a survey.** Frontiers of Information Technology & Electronic Engineering 2018 [paper](https://arxiv.org/abs/1802.00614) [bib](/bib/Machine-Learning/Interpretability-and-Analysis/Zhang2018Visual.md)

    *Quanshi Zhang, Songchun Zhu*

14. **Visualisation of Pareto Front Approximation: A Short Survey and Empirical Comparisons.** arXiv 2019 [paper](https://arxiv.org/abs/1903.01768) [bib](/bib/Machine-Learning/Interpretability-and-Analysis/Gao2019Visualisation.md)

    *Huiru Gao, Haifeng Nie, Ke Li*

15. **When will the mist clear? On the Interpretability of Machine Learning for Medical Applications: a survey.** arxiv 2020 [paper](https://arxiv.org/ftp/arxiv/papers/2010/2010.00353.pdf) [bib](/bib/Machine-Learning/Interpretability-and-Analysis/Luna2020When.md)

    *Antonio-Jesús Banegas-Luna, Jorge Peña-García, Adrian Iftene, Fiorella Guadagni, Patrizia Ferroni, Noemi
Scarpato, Fabio Massimo Zanzotto, Andrés Bueno-Crespo, Horacio
Pérez-Sánchez*

#### [Meta Learning](#content)

1. **A Comprehensive Overview and Survey of Recent Advances in Meta-Learning.** arXiv 2020 [paper](https://arxiv.org/abs/2004.11149) [bib](/bib/Machine-Learning/Meta-Learning/Peng2020A.md)

    *Huimin Peng*

2. **Meta-learning for Few-shot Natural Language Processing: A Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2007.09604) [bib](/bib/Machine-Learning/Meta-Learning/Wenpeng2020Meta.md)

    *Wenpeng Yin*

3. **Meta-Learning in Neural Networks: A Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2004.05439) [bib](/bib/Machine-Learning/Meta-Learning/Hospedales2020Meta-Learning.md)

    *Timothy M. Hospedales, Antreas Antoniou, Paul Micaelli, Amos J. Storkey*

4. **Meta-Learning: A Survey.** arXiv 2018 [paper](https://arxiv.org/abs/1810.03548) [bib](/bib/Machine-Learning/Meta-Learning/Vanschoren2018Meta-Learning.md)

    *Joaquin Vanschoren*

#### [Metric Learning](#content)

1. **A Survey on Metric Learning for Feature Vectors and Structured Data.** arXiv 2013 [paper](https://arxiv.org/abs/1306.6709) [bib](/bib/Machine-Learning/Metric-Learning/Bellet2013A.md)

    *Aurelien Bellet, Amaury Habrard, Marc Sebban*

2. **A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms and Experiments.** arXiv 2018 [paper](https://arxiv.org/abs/1812.05944) [bib](/bib/Machine-Learning/Metric-Learning/Suarez2018A.md)

    *Juan Luis Suarez, Salvador Garcia, Francisco Herrera*

#### [ML Applications](#content)

1. **360 degree view of cross-domain opinion classification: a survey.** Artificial Intelligence Review 2020 [paper](https://link.springer.com/article/10.1007/s10462-020-09884-9) [bib](/bib/Machine-Learning/ML-Applications/Singh2020degree.md)

    *Rahul Kumar Singh，Manoj Kumar Sachan，R. B. Patel*

2. **A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications.** Neural Networks 2019 [paper](https://arxiv.org/abs/1905.11437) [bib](/bib/Machine-Learning/ML-Applications/Silva2019A.md)

    *Leonardo Enzo Brito da Silva, Islam Elnabarawy, Donald C. Wunsch II*

3. **A Survey of Machine Learning Methods and Challenges for Windows Malware Classification.** arXiv 2020 [paper](https://arxiv.org/abs/2006.09271) [bib](/bib/Machine-Learning/ML-Applications/Raff2020A.md)

    *Edward Raff, Charles Nicholas*

4. **A survey on applications of augmented, mixed andvirtual reality for nature and environment.** arXiv 2020 [paper](https://arxiv.org/abs/2008.12024) [bib](/bib/Machine-Learning/ML-Applications/Rambach2020A.md)

    *Jason Rambach, Gergana Lilligreen, Alexander Sch盲fer, Ramya Bankanal, Alexander Wiebel, Didier Stricker*

5. **A survey on deep hashing for image retrieval.** arXiv 2020 [paper](https://arxiv.org/abs/2006.05627) [bib](/bib/Machine-Learning/ML-Applications/Zhang2020A.md)

    *Xiaopeng Zhang*

6. **A Survey on Deep Learning based Brain-Computer Interface: Recent Advances and New Frontiers.** arXiv 2019 [paper](https://arxiv.org/abs/1905.04149v1) [bib](/bib/Machine-Learning/ML-Applications/Zhang2019A.md)

    *Xiang Zhang, Lina Yao, Xianzhi Wang, Jessica J M Monaghan, David Mcalpine, Yu Zhang*

7. **A Survey on Deep Learning in Medical Image Analysis.** Medical Image Analysis 2017 [paper](https://arxiv.org/abs/1702.05747) [bib](/bib/Machine-Learning/ML-Applications/Litjens2017A.md)

    *Geert J S Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud A A Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A W M Van Der Laak, Bram Van Ginneken, Clara I Sanchez*

8. **A Survey on Machine Learning Applied to Dynamic Physical Systems.** arxiv 2020 [paper](https://arxiv.org/pdf/2009.09719.pdf) [bib](/bib/Machine-Learning/ML-Applications/Verma2020A.md)

    *Sagar Verma*

9. **Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial.** IEEE 2019 [paper](https://ieeexplore.ieee.org/document/8755300) [bib](/bib/Machine-Learning/ML-Applications/Chen2019Artificial.md)

    *Mingzhe Chen, Ursula Challita, Walid Saad, Changchuan Yin, Mérouane Debbah*

10. **How Developers Iterate on Machine Learning Workflows -- A Survey of the Applied Machine Learning Literature.** arXiv 2018 [paper](https://arxiv.org/abs/1803.10311) [bib](/bib/Machine-Learning/ML-Applications/Xin2018How.md)

    *Doris Xin, Litian Ma, Shuchen Song, Aditya G. Parameswaran*

11. **Local Differential Privacy and Its Applications: A Comprehensive Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2008.03686) [bib](/bib/Machine-Learning/ML-Applications/Yang2020Local.md)

    *Mengmeng Yang, Lingjuan Lyu, Jun Zhao, Tianqing Zhu, Kwok-Yan Lam*

12. **Machine Learning Aided Static Malware Analysis: A Survey and Tutorial.** arXiv 2018 [paper](https://arxiv.org/abs/1808.01201) [bib](/bib/Machine-Learning/ML-Applications/Shalaginov2018Machine.md)

    *Andrii Shalaginov, Sergii Banin, Ali Dehghantanha, Katrin Franke*

13. **Machine Learning for Survival Analysis: A Survey.** arXiv 2017 [paper](https://arxiv.org/abs/1708.04649) [bib](/bib/Machine-Learning/ML-Applications/Wang2017Machine.md)

    *Ping Wang, Yan Li, Chandan K. Reddy*

14. **The Creation and Detection of Deepfakes:A Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2004.11138v1) [bib](/bib/Machine-Learning/ML-Applications/Mirsky2020The.md)

    *Yisroel Mirsky, Wenke Lee*

15. **The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey.** arxiv 2019 [paper](https://arxiv.org/pdf/1911.02621v2.pdf) [bib](/bib/Machine-Learning/ML-Applications/Ibitoye2019The.md)

    *Olakunle Ibitoye, Rana Abou-Khamis, Ashraf Matrawy, M. Omair Shafiq*

#### [Model Compression and Acceleration](#content)

1. **A Survey of Model Compression and Acceleration for Deep Neural Networks.** IEEE 2017 [paper](https://arxiv.org/abs/1710.09282) [bib](/bib/Machine-Learning/Model-Compression-and-Acceleration/Cheng2017A.md)

    *Yu Cheng, Duo Wang, Pan Zhou, Tao Zhang*

2. **A Survey on Methods and Theories of Quantized Neural Networks.** arXiv 2018 [paper](https://arxiv.org/abs/1808.04752) [bib](/bib/Machine-Learning/Model-Compression-and-Acceleration/Guo2018A.md)

    *Yunhui Guo*

3. **An Overview of Neural Network Compression.** arXiv 2020 [paper](https://arxiv.org/abs/2006.03669) [bib](/bib/Machine-Learning/Model-Compression-and-Acceleration/Neill2020An.md)

    *James O' Neill*

4. **Compression of Deep Learning Models for Text: A Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2008.05221) [bib](/bib/Machine-Learning/Model-Compression-and-Acceleration/Gupta2020Compression.md)

    *Manish Gupta, Puneet Agrawal*

5. **Knowledge Distillation: A Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2006.05525) [bib](/bib/Machine-Learning/Model-Compression-and-Acceleration/Gou2020Knowledge.md)

    *Jianping Gou, Baosheng Yu, Stephen John Maybank, Dacheng Tao*

6. **Machine Learning at the Network Edge: A Survey.** arxiv 2020 [paper](https://arxiv.org/pdf/1908.00080v3.pdf) [bib](/bib/Machine-Learning/Model-Compression-and-Acceleration/Murshed2020Machine.md)

    *M.G. Sarwar Murshed, Christopher Murphy, Daqing Hou, Nazar Khan, Ganesh Ananthanarayanan, Faraz Hussain*

7. **Pruning Algorithms to Accelerate Convolutional Neural Networks for Edge Applications: A Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2005.04275) [bib](/bib/Machine-Learning/Model-Compression-and-Acceleration/Liu2020Pruning.md)

    *Jiayi Liu, Samarth Tripathi, Unmesh Kurup, Mohak Shah*

8. **Survey of Machine Learning Accelerators.** IEEE 2020 [paper](http://arxiv.org/pdf/2009.00993v1.pdf) [bib](/bib/Machine-Learning/Model-Compression-and-Acceleration/Reuther2020Survey.md)

    *Albert Reuther, Peter Michaleas, Michael Jones, Vijay Gadepally, Siddharth Samsi, Jeremy Kepner*

#### [Multi-Task and Multi-View Learning](#content)

1. **A Brief Review on Multi-Task Learning.** Multimedia Tools and Applications 2018 [paper](https://www.researchgate.net/publication/326903979_A_brief_review_on_multi-task_learning) [bib](/bib/Machine-Learning/Multi-Task-and-Multi-View-Learning/Thung2018A.md)

    *Kimhan Thung, Chong Yaw Wee*

2. **A Survey on Multi-Task Learning.** arXiv 2017 [paper](https://arxiv.org/abs/1707.08114) [bib](/bib/Machine-Learning/Multi-Task-and-Multi-View-Learning/Zhang2017A.md)

    *Yu Zhang, Qiang Yang*

3. **A Survey on Multi-view Learning.** Computer ence 2013 [paper](https://arxiv.org/abs/1304.5634) [bib](/bib/Machine-Learning/Multi-Task-and-Multi-View-Learning/Xu2013A.md)

    *Chang Xu, Dacheng Tao, Chao Xu*

4. **An overview of multi-task learning.** National Science Review 2018 [paper](https://academic.oup.com/nsr/article/5/1/30/4101432) [bib](/bib/Machine-Learning/Multi-Task-and-Multi-View-Learning/Zhang2018An.md)

    *Yu Zhang, Qiang Yang*

5. **An Overview of Multi-Task Learning in Deep Neural Networks.** arXiv 2017 [paper](https://arxiv.org/abs/1706.05098) [bib](/bib/Machine-Learning/Multi-Task-and-Multi-View-Learning/Ruder2017An.md)

    *Sebastian Ruder*

6. **Multi-Task Learning for Dense Prediction Tasks: A Survey.** arxiv 2020 [paper](https://arxiv.org/pdf/2004.13379v2.pdf) [bib](/bib/Machine-Learning/Multi-Task-and-Multi-View-Learning/Vandenhende2020Muliti.md)

    *Simon Vandenhende, Stamatios Georgoulis, Wouter Van Gansbeke, Marc Proesmans, Dengxin Dai, Luc Van Gool*

7. **Multi-Task Learning with Deep Neural Networks: A Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2009.09796) [bib](/bib/Machine-Learning/Multi-Task-and-Multi-View-Learning/Crawshaw2020Multi.md)

    *Michael Crawshaw*

8. **Revisiting Multi-Task Learning in the Deep Learning Era.** arXiv 2020 [paper](https://arxiv.org/abs/2004.13379) [bib](/bib/Machine-Learning/Multi-Task-and-Multi-View-Learning/Vandenhende2020Knowledge.md)

    *Simon Vandenhende, Stamatios Georgoulis, Marc Proesmans, Dengxin Dai, Luc Van Gool*

#### [Online Learning](#content)

1. **A Survey of Algorithms and Analysis for Adaptive Online Learning.** Journal of Machine Learning Research 2017 [paper](https://arxiv.org/abs/1403.3465) [bib](/bib/Machine-Learning/Online-Learning/McMahan2017A.md)

    *H. Brendan McMahan*

2. **Online Learning: A Comprehensive Survey.** arXiv 2018 [paper](https://arxiv.org/abs/1802.02871) [bib](/bib/Machine-Learning/Online-Learning/Hoi2018Online.md)

    *Steven C.H. Hoi, Doyen Sahoo, Jing Lu, Peilin Zhao*

3. **Preference-based Online Learning with Dueling Bandits: A Survey.** arXiv 2018 [paper](https://arxiv.org/abs/1807.11398) [bib](/bib/Machine-Learning/Online-Learning/Fekete2018Preference-based.md)

    *Robert Busa-Fekete, Eyke Hüllermeier, Adil El Mesaoudi-Paul*

#### [Optimization](#content)

1. **A Survey of Optimization Methods from a Machine Learning Perspective.** IEEE 2019 [paper](https://arxiv.org/abs/1906.06821) [bib](/bib/Machine-Learning/Optimization/Sun2019A.md)

    *Shiliang Sun, Zehui Cao, Han Zhu, Jing Zhao*

2. **A Systematic and Meta-analysis Survey of Whale Optimization Algorithm.** Computational Intelligence and Neuroscience 2019 [paper](https://arxiv.org/abs/1903.08763) [bib](/bib/Machine-Learning/Optimization/Mohammed2019A.md)

    *Hardi M. Mohammed, Shahla U. Umar, Tarik A. Rashid*

3. **An overview of gradient descent optimization algorithms.** arXiv 2017 [paper](https://arxiv.org/abs/1609.04747) [bib](/bib/Machine-Learning/Optimization/Ruder2016An.md)

    *Sebastian Ruder*

4. **Convex Optimization Overview.** IEEE 2008 [paper](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.142.6470) [bib](/bib/Machine-Learning/Optimization/Kolter2008Convex.md)

    *Kolter Zico, Lee Honglak*

5. **Gradient Boosting Machine: A Survey.** arXiv 2019 [paper](https://arxiv.org/abs/1908.06951) [bib](/bib/Machine-Learning/Optimization/He2019Gradient.md)

    *Zhiyuan He, Danchen Lin, Thomas Lau, Mike Wu*

6. **Optimization for deep learning: theory and algorithms.** arXiv 2019 [paper](https://arxiv.org/abs/1912.08957) [bib](/bib/Machine-Learning/Optimization/Sun2019Optimization.md)

    *Ruoyu Sun*

7. **Optimization Models for Machine Learning: A Survey.** arXiv 2019 [paper](https://arxiv.org/abs/1901.05331) [bib](/bib/Machine-Learning/Optimization/Gambella2019Optimization.md)

    *Claudio Gambella, Bissan Ghaddar, Joe Naoum-Sawaya*

8. **Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives.** Machine Learning & Knowledge Extraction 2019 [paper](https://arxiv.org/abs/1804.05319) [bib](/bib/Machine-Learning/Optimization/Sengupta2019Particle.md)

    *Saptarshi Sengupta, Sanchita Basak, Richard Alan Peters II*

#### [Semi-Supervised and Unsupervised Learning](#content)

1. **A brief introduction to weakly supervised learning.** National Science Review 2018 [paper](https://cs.nju.edu.cn/_upload/tpl/01/0b/267/template267/zhouzh.files/publication/nsr18.pdf) [bib](/bib/Machine-Learning/Semi-Supervised-and-Unsupervised-Learning/Zhou2018A.md)

    *Zhihua Zhou*

2. **A Survey of Unsupervised Dependency Parsing.** COLING 2020 [paper](https://arxiv.org/pdf/2010.01535.pdf) [bib](/bib/Machine-Learning/Semi-Supervised-and-Unsupervised-Learning/Han2020A.md)

    *Wenjuan Han, Yong Jiang, Hwee Tou Ng, Kewei Tu*

3. **A survey on Semi-, Self- and Unsupervised Learning for Image Classification.**  2020 [paper](https://arxiv.org/abs/2002.08721) [bib](/bib/Machine-Learning/Semi-Supervised-and-Unsupervised-Learning/Schmarje2020Survey.md)

    *Lars Schmarje, Monty Santarossa, Simon-Martin Schröder, Reinhard Koch*

4. **A Survey on Semi-Supervised Learning Techniques.** International Journal of Computer Trends & Technology 2014 [paper](https://arxiv.org/abs/1402.4645) [bib](/bib/Machine-Learning/Semi-Supervised-and-Unsupervised-Learning/Prakash2014A.md)

    *V. Jothi Prakash, Dr. L.M. Nithya*

5. **Improvability Through Semi-Supervised Learning: A Survey of Theoretical Results.** arXiv 2019 [paper](https://arxiv.org/abs/1908.09574) [bib](/bib/Machine-Learning/Semi-Supervised-and-Unsupervised-Learning/Mey2019Improvability.md)

    *Alexander Mey, Marco Loog*

6. **Learning from positive and unlabeled data: a survey.** Machine Learning 2020 [paper](https://arxiv.org/abs/1811.04820) [bib](/bib/Machine-Learning/Semi-Supervised-and-Unsupervised-Learning/Bekker2020Learning.md)

    *Jessa Bekker, Jesse Davis*

#### [Transfer Learning](#content)

1. **A Comprehensive Survey on Transfer Learning.** arXiv 2019 [paper](https://arxiv.org/abs/1911.02685) [bib](/bib/Machine-Learning/Transfer-Learning/Zhuang2019A.md)

    *Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, Qing He*

2. **A Survey of Unsupervised Deep Domain Adaptation.** arXiv 2020 [paper](https://arxiv.org/abs/1812.02849) [bib](/bib/Machine-Learning/Transfer-Learning/Wilson2020A.md)

    *Garrett Wilson, Diane J. Cook*

3. **A Survey on Deep Transfer Learning.** International Conference on Artificial Neural Networks 2018 [paper](https://arxiv.org/abs/1808.01974) [bib](/bib/Machine-Learning/Transfer-Learning/Tan2018A.md)

    *Chuanqi Tan, Fuchun Sun, Tao Kong, Wenchang Zhang, Chao Yang, Chunfang Liu*

4. **A survey on domain adaptation theory: learning bounds and theoretical guarantees.** arXiv 2020 [paper](https://arxiv.org/abs/2004.11829) [bib](/bib/Machine-Learning/Transfer-Learning/Redko2020A.md)

    *Ievgen Redko, Emilie Morvant, Amaury Habrard, Marc Sebban, Younès Bennani*

5. **A Survey on Transfer Learning in Natural Language Processing.** arXiv 2020 [paper](https://arxiv.org/abs/2007.04239) [bib](/bib/Machine-Learning/Transfer-Learning/Alyafeai2020A.md)

    *Zaid Alyafeai, Maged Saeed AlShaibani, Irfan Ahmad*

6. **Evolution of transfer learning in natural language processing.** arXiv 2019 [paper](https://arxiv.org/abs/1910.07370) [bib](/bib/Machine-Learning/Transfer-Learning/Malte2019Evolution.md)

    *Aditya Malte, Pratik Ratadiya*

7. **Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.** arXiv 2019 [paper](https://arxiv.org/pdf/1910.10683.pdf) [bib](/bib/Machine-Learning/Transfer-Learning/Raffel2019Explorin.md)

    *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu*

8. **Neural Unsupervised Domain Adaptation in NLP - A Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2006.00632) [bib](/bib/Machine-Learning/Transfer-Learning/Ramponi2020Neural.md)

    *Alan Ramponi, Barbara Plank*

9. **Overcoming Negative Transfer: A Survey.** arxiv 2020 [paper](http://arxiv.org/pdf/2009.00909v1.pdf) [bib](/bib/Machine-Learning/Transfer-Learning/Zhang2020Overcoming.md)

    *Wen Zhang, Lingfei Deng, Dongrui Wu*

10. **Transfer Adaptation Learning: A Decade Survey.** arXiv 2019 [paper](https://arxiv.org/pdf/1903.04687.pdf) [bib](/bib/Machine-Learning/Transfer-Learning/Zhang2019Transfer.md)

    *Lei Zhang*

11. **Transfer Learning in Deep Reinforcement Learning: A Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2009.07888) [bib](/bib/Machine-Learning/Transfer-Learning/Zhu2020Transfer.md)

    *Zhuangdi Zhu, Kaixiang Lin, Jiayu Zhou*

#### [Trustworthy Machine Learning](#content)

1. **A Survey on Bias and Fairness in Machine Learning.** arXiv 2019 [paper](https://arxiv.org/abs/1908.09635) [bib](/bib/Machine-Learning/Trustworthy-Machine-Learning/Mehrabi2019A.md)

    *Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, Aram Galstyan*

2. **Differential Privacy and Machine Learning: a Survey and Review.** Eprint Arxiv 2014 [paper](https://arxiv.org/abs/1412.7584) [bib](/bib/Machine-Learning/Trustworthy-Machine-Learning/Ji2014Differential.md)

    *Zhanglong Ji, Zachary C. Lipton, Charles Elkan*

3. **Tutorial: Safe and Reliable Machine Learning.** ACM 2019 [paper](https://arxiv.org/abs/1904.07204) [bib](/bib/Machine-Learning/Trustworthy-Machine-Learning/Saria2019Tutorial.md)

    *Suchi Saria, Adarsh Subbaswamy*



## Team Members

The project is maintained by 

*Ziyang Wang, Nuo Xu, Bei Li, Yinqiao Li, Quan Du, Tong Xiao, and Jingbo Zhu*

*Natural Language Processing Lab., School of Computer Science and Engineering, Northeastern University*

*NiuTrans Research*

Please feel free to contact us if you have any questions (wangziyang [at] stumail.neu.edu.cn or libei_neu [at] outlook.com).

## Acknowledge

We would like to thank the people who have contributed to this project. They are

*Shuhan Zhou, Xin Zeng, Laohu Wang, Chenglong Wang, Xiaoqian Liu, Xuanjun Zhou, Jingnan Zhang, Yongyu Mu, Zefan Zhou, Yanhong Jiang, Xinyang Zhu, Xingyu Liu, Dong Bi, Ping Xu, Zijian Li, Fengning Tian, Hui Liu, Kai Feng, Yuhao Zhang, Chi Hu, Di Yang, Lei Zheng, Hexuan Chen, Zeyang Wang, Tengbo Liu, Xia Meng, Weiqiao Shan, Tao Zhou, Runzhe Cao, Yingfeng Luo, Binghao Wei, Wandi Xu, Yan Zhang, Yichao Wang, Mengyu Ma, Zihao Liu*