# state-of-the-art-result-for-machine-learning-problems **Repository Path**: ifquant/state-of-the-art-result-for-machine-learning-problems ## Basic Information - **Project Name**: state-of-the-art-result-for-machine-learning-problems - **Description**: This repository provides state of the art (SoTA) results for all machine learning problems. We do our best to keep this repository up to date. If you do find a problem's SoTA result is out of date or missing, please raise this as an issue or submit Google form (with this information: research paper name, dataset, metric, source code and year). We will fix it immediately. - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2021-07-31 - **Last Updated**: 2021-07-31 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # State-of-the-art result for all Machine Learning Problems ### LAST UPDATE: 6th November 2018 ### NEWS: I am looking for a Collaborator esp who does research in NLP, Computer Vision and Reinforcement learning. If you are not a researcher, but you are willing, contact me. Email me: yxt.stoaml@gmail.com This repository provides state-of-the-art (SoTA) results for all machine learning problems. We do our best to keep this repository up to date. If you do find a problem's SoTA result is out of date or missing, please raise this as an issue (with this information: research paper name, dataset, metric, source code and year). We will fix it immediately. You can also submit this [Google Form](https://docs.google.com/forms/d/e/1FAIpQLSe_fFZVCeCVRGGgOQIpoQSXY7mZWynsx7g6WxZEVpO5vJioUA/viewform?embedded=true) if you are new to Github. This is an attempt to make one stop for all types of machine learning problems state of the art result. I can not do this alone. I need help from everyone. Please submit the Google form/raise an issue if you find SOTA result for a dataset. Please share this on Twitter, Facebook, and other social media. This summary is categorized into: - [Supervised Learning](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems#supervised-learning) - [Speech](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems#speech) - [Computer Vision](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems#computer-vision) - [NLP](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems#nlp) - [Semi-supervised Learning](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems#semi-supervised-learning) - Computer Vision - [Unsupervised Learning](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems#unsupervised-learning) - Speech - Computer Vision - [NLP](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems/blob/master/README.md#nlp-1) - [Transfer Learning](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems#transfer-learning) - [Reinforcement Learning](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems#reinforcement-learning) ## Supervised Learning ### NLP #### 1. Language Modelling
Research Paper Datasets Metric Source Code Year
BREAKING THE SOFTMAX BOTTLENECK: A HIGH-RANK RNN LANGUAGE MODEL
  • PTB
  • WikiText-2
  • Perplexity: 47.69
  • Perplexity: 40.68
Pytorch 2017
DYNAMIC EVALUATION OF NEURAL SEQUENCE MODELS
  • PTB
  • WikiText-2
  • Perplexity: 51.1
  • Perplexity: 44.3
Pytorch 2017
Averaged Stochastic Gradient Descent
with Weight Dropped LSTM or QRNN
  • PTB
  • WikiText-2
  • Perplexity: 52.8
  • Perplexity: 52.0
Pytorch 2017
FRATERNAL DROPOUT
  • PTB
  • WikiText-2
  • Perplexity: 56.8
  • Perplexity: 64.1
Pytorch 2017
Factorization tricks for LSTM networks One Billion Word Benchmark Perplexity: 23.36 Tensorflow 2017
#### 2. Machine Translation
Research Paper Datasets Metric Source Code Year
WEIGHTED TRANSFORMER NETWORK FOR MACHINE TRANSLATION
  • WMT 2014 English-to-French
  • WMT 2014 English-to-German
  • BLEU: 41.4
  • BLEU: 28.9
2017
Attention Is All You Need
  • WMT 2014 English-to-French
  • WMT 2014 English-to-German
  • BLEU: 41.0
  • BLEU: 28.4
2017
NON-AUTOREGRESSIVE NEURAL MACHINE TRANSLATION
  • WMT16 Ro→En
  • BLEU: 31.44
2017
Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets
  • NIST02
  • NIST03
  • NIST04
  • NIST05
  • 38.74
  • 36.01
  • 37.54
  • 33.76
    		• 2017
    #### 3. Text Classification
    Research Paper Datasets Metric Source Code Year
    Learning Structured Text Representations Yelp Accuracy: 68.6 2017
    Attentive Convolution Yelp Accuracy: 67.36 2017
    #### 4. Natural Language Inference Leader board: [Stanford Natural Language Inference (SNLI)](https://nlp.stanford.edu/projects/snli/) [MultiNLI](https://www.kaggle.com/c/multinli-matched-open-evaluation/leaderboard)
    Research Paper Datasets Metric Source Code Year
    NATURAL LANGUAGE INFERENCE OVER INTERACTION SPACE Stanford Natural Language Inference (SNLI) Accuracy: 88.9 Tensorflow 2017
    BERT-LARGE (ensemble) Multi-Genre Natural Language Inference (MNLI)
    • Matched accuracy: 86.7
    • Mismatched accuracy: 85.9
    		 2018
    #### 5. Question Answering Leader Board [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/)
    Research Paper Datasets Metric Source Code Year
    BERT-LARGE (ensemble) The Stanford Question Answering Dataset
    • Exact Match: 87.4
    • F1: 93.2
    		2018
    #### 6. Named entity recognition
    Research Paper Datasets Metric Source Code Year
    Named Entity Recognition in Twitter using Images and Text Ritter
    • F-measure: 0.59
    		NOT FOUND 2017
    #### 7. Abstractive Summarization Research Paper | Datasets | Metric | Source Code | Year ------------ | ------------- | ------------ | ------------- | ------------- [Cutting-off redundant repeating generations
    for neural abstractive summarization](https://aclanthology.info/pdf/E/E17/E17-2047.pdf) | | | NOT YET AVAILABLE | 2017 [Convolutional Sequence to Sequence](https://arxiv.org/pdf/1705.03122.pdf) | | | [PyTorch](https://github.com/facebookresearch/fairseq-py) | 2017 #### 8. Dependency Parsing Research Paper | Datasets | Metric | Source Code | Year ------------ | ------------- | ------------ | ------------- | ------------- [Globally Normalized Transition-Based Neural Networks](https://arxiv.org/pdf/1603.06042.pdf) | | | | ### Computer Vision #### 1. Classification            
    Research Paper Datasets Metric Source Code Year
    Dynamic Routing Between Capsules
    • MNIST
    • Test Error: 0.25±0.005
    		2017
    High-Performance Neural Networks for Visual Object Classification
    • NORB
    • Test Error: 2.53 ± 0.40
    		2011
    ShakeDrop regularization
    • CIFAR-10
    • CIFAR-100
    • Test Error: 2.31%
    • Test Error: 12.19%
    		2017
    Aggregated Residual Transformations for Deep Neural Networks
    • CIFAR-10
    • Test Error: 3.58%
    		2017
    Random Erasing Data Augmentation
    • CIFAR-10
    • CIFAR-100
    • Fashion-MNIST
    • Test Error: 3.08%
    • Test Error: 17.73%
    • Test Error: 3.65%
    		Pytorch 2017
    EraseReLU: A Simple Way to Ease the Training of Deep Convolution Neural Networks
    • CIFAR-10
    • CIFAR-100
    • Test Error: 3.56%
    • Test Error: 16.53%
    		Pytorch 2017
    Dynamic Routing Between Capsules
    • MultiMNIST
    • Test Error: 5%
    		2017
    Learning Transferable Architectures for Scalable Image Recognition
    • ImageNet-1k
    • Top-1 Error:17.3
    		2017
    Squeeze-and-Excitation Networks
    • ImageNet-1k
    • Top-1 Error: 18.68
    		2017
    Aggregated Residual Transformations for Deep Neural Networks
    • ImageNet-1k
    • Top-1 Error: 20.4%
    		2016
    #### 2. Instance Segmentation
    Research Paper Datasets Metric Source Code Year
    Mask R-CNN
    • COCO
    • Average Precision: 37.1%
    		2017
    #### 3. Visual Question Answering
    Research Paper Datasets Metric Source Code Year
    Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge
    • VQA
    • Overall score: 69
    		2017
    #### 4. Person Re-identification      
    Research Paper Datasets Metric Source Code Year
    Random Erasing Data Augmentation
    • Rank-1: 89.13 mAP: 83.93
    • Rank-1: 84.02 mAP: 78.28
    • labeled (Rank-1: 63.93 mAP: 65.05) detected (Rank-1: 64.43 mAP: 64.75)
    		Pytorch 2017
    ### Speech [Speech SOTA](https://github.com/syhw/wer_are_we) #### 1. ASR
    Research Paper Datasets Metric Source Code Year
    The Microsoft 2017 Conversational Speech Recognition System
    • Switchboard Hub5'00
    • WER: 5.1
    		2017
    The CAPIO 2017 Conversational Speech Recognition System
    • Switchboard Hub5'00
    • WER: 5.0
    		2017
    ## Semi-supervised Learning #### Computer Vision      
    Research Paper Datasets Metric Source Code Year
    DISTRIBUTIONAL SMOOTHINGWITH VIRTUAL ADVERSARIAL TRAINING
    • SVHN
    • NORB
    • Test error: 24.63
    • Test error: 9.88
    		Theano 2016
    Virtual Adversarial Training: a Regularization Method for Supervised and Semi-supervised Learning
    • MNIST
    • Test error: 1.27
    		2017
    Few Shot Object Detection
    • VOC2007
    • VOC2012
    • mAP : 41.7
    • mAP : 35.4
    		2017
    Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro
    • Rank-1: 83.97 mAP: 66.07
    • Rank-1: 84.6 mAP: 87.4
    • Rank-1: 67.68 mAP: 47.13
      •          
    • Test Accuracy: 84.4
    		Matconvnet 2017
    ## Unsupervised Learning #### Computer Vision ##### 1. Generative Model
    Research Paper Datasets Metric Source Code Year
    PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION Unsupervised CIFAR 10 Inception score: 8.80 Theano 2017
    ### NLP #### Machine Translation
    Research Paper Datasets Metric Source Code Year
    UNSUPERVISED MACHINE TRANSLATION USING MONOLINGUAL CORPORA ONLY
    • Multi30k-Task1(en-fr fr-en de-en en-de)
    • BLEU:(32.76 32.07 26.26 22.74)
    		2017
    Unsupervised Neural Machine Translation with Weight Sharing
    • WMT14(en-fr fr-en)
    • WMT16 (de-en en-de)
    • BLEU:(16.97 15.58)
    • BLEU:(14.62 10.86)
    		2018
    ## Transfer Learning
    Research Paper Datasets Metric Source Code Year
    One Model To Learn Them All
    • WMT EN → DE
    • WMT EN → FR (BLEU)
    • ImageNet (top-5 accuracy)
    • BLEU: 21.2
    • BLEU:30.5
    • 86%
    		2017
    ## Reinforcement Learning
    Research Paper Datasets Metric Source Code Year
    Mastering the game of Go without human knowledge the game of Go ElO Rating: 5185 2017
    Email: yxt.stoaml@gmail.com