# state-of-the-art-result-for-machine-learning-problems **Repository Path**: peiwen_liu/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 If you do find a problem's SoTA result is out of date or missing, please raise this as an issue or submit Google form (with this information: research paper name, dataset, metric, source code and year).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**: 2021-07-13 - **Last Updated**: 2021-07-13 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # State-of-the-art result for all Machine Learning Problems ### LAST UPDATE: 9th November, 2017 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 If you do find a problem's SoTA result is out of date or missing, please raise this as an issue (with this information: research paper name, dataset, metric, source code and year).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/1FAIpQLSeMnkZ24YqiNkQEER_ihckenijBP7GpQpv8ZrkBnY7ythCItw/viewform?usp=sf_link) 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 - [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
DYNAMIC EVALUATION OF NEURAL SEQUENCE MODELS
  • PTB
  • WikiText-2
  • Preplexity: 51.1
  • Perplexity: 44.3
Pytorch 2017
Averaged Stochastic Gradient Descent
with Weight Dropped LSTM or QRNN
  • PTB
  • WikiText-2
  • Preplexity: 52.8
  • Perplexity: 52.0
Pytorch 2017
FRATERNAL DROPOUT
  • PTB
  • WikiText-2
  • Preplexity: 56.8
  • Perplexity: 64.1
Pytorch 2017
Factorization tricks for LSTM networks One Billion Word Benchmark Preplexity: 23.36 Tensorflow 2017
#### 2. Machine Translation
Research Paper Datasets Metric Source Code Year
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
NOT YET RELEASED 2017
#### 3. Text Classification
Research Paper Datasets Metric Source Code Year
Learning Structured Text Representations Yelp Accuracy: 68.6 NOT YET AVAILABLE 2017
Attentive Convolution Yelp Accuracy: 67.36 NOT YET AVAILABLE 2017
#### 4. Natural Language Inference Research Paper | Datasets | Metric | Source Code | Year ------------ | ------------- | ------------ | ------------- | ------------- [DiSAN: Directional Self-Attention Network
for RNN/CNN-free Language Understanding](https://arxiv.org/pdf/1709.04696.pdf) | Stanford Natural Language Inference (SNLI) | Accuracy: 51.72 | NOT YET AVAILABLE | 2017 #### 5. Question Answering Research Paper | Datasets | Metric | Source Code | Year ------------ | ------------- | ------------ | ------------- | ------------- [Interactive AoA Reader+ (ensemble)](https://rajpurkar.github.io/SQuAD-explorer/) | The Stanford Question Answering Dataset | | NOT YET AVAILABLE | 2017 #### 6. Named entity recognition Research Paper | Datasets | Metric | Source Code | Year ------------ | ------------- | ------------ | ------------- | ------------- [Named Entity Recognition in Twitter
using Images and Text](https://arxiv.org/pdf/1710.11027.pdf) | Ritter | F-measure: 0.59 | NOT YET AVAILABLE | 2017 ### Computer Vision #### 1. Classification Research Paper | Datasets | Metric | Source Code | Year ------------ | ------------- | ------------ | ------------- | ------------- [Dynamic Routing Between Capsules](https://arxiv.org/pdf/1710.09829.pdf) | MNIST | Test Error: 0.25±0.005 | | 2017 [High-Performance Neural Networks for Visual Object Classification](https://arxiv.org/pdf/1102.0183.pdf) | NORB | Test Error: 2.53 ± 0.40| NOT FOUND | 2011 [Dynamic Routing Between Capsules](https://arxiv.org/pdf/1710.09829.pdf) | CIFAR-10 | Test Error: 10.6% | | 2017 [Dynamic Routing Between Capsules](https://arxiv.org/pdf/1710.09829.pdf) | MultiMNIST | Test Error: 5% | | 2017 ### Speech #### 1. ASR Research Paper | Datasets | Metric | Source Code | Year ------------ | ------------- | ------------ | ------------- | ------------- [The Microsoft 2017 Conversational Speech Recognition System](https://arxiv.org/pdf/1708.06073.pdf) | Switchboard Hub5'00 | WER: 5.1 | NOT FOUND | 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
NOT FOUND 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
## Transfer Learning ## Reinforcement Learning Email: redditsota@gmail.com