RedditSota / state-of-the-art-result-for-machine-learning-problems
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 If you do find a problem's SoTA result is out of date or missing, please raise this as an issue or submit Google form (with this information: research paper name, dataset, metric, source code and year).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.
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Repository Summary (README)
PreviewState-of-the-art result for all Machine Learning Problems
LAST UPDATE: 20th Februray 2019
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 If you do find a problem's SoTA result is out of date or missing, please raise this as an issue (with this information: research paper name, dataset, metric, source code and year).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 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
- Semi-supervised Learning
- Computer Vision
- Unsupervised Learning
- Speech
- Computer Vision
- NLP
- Transfer Learning
- Reinforcement Learning
Supervised Learning
NLP
1. Language Modelling
| Research Paper | Datasets | Metric | Source Code | Year |
|---|---|---|---|---|
| Language Models are Unsupervised Multitask Learners |
|
| Tensorflow | 2019 |
| BREAKING THE SOFTMAX BOTTLENECK: A HIGH-RANK RNN LANGUAGE MODEL |
|
| Pytorch | 2017 |
| DYNAMIC EVALUATION OF NEURAL SEQUENCE MODELS |
|
| Pytorch | 2017 |
| Averaged Stochastic Gradient Descent with Weight Dropped LSTM or QRNN |
|
| Pytorch | 2017 |
| FRATERNAL DROPOUT |
|
| 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 |
|---|---|---|---|---|
| Understanding Back-Translation at Scale |
|
| 2018 | |
| WEIGHTED TRANSFORMER NETWORK FOR MACHINE TRANSLATION |
|
| 2017 | |
| Attention Is All You Need |
|
| 2017 | |
| NON-AUTOREGRESSIVE NEURAL MACHINE TRANSLATION |
|
| 2017 | |
| Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets |
| 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)
| 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) |
| 2018 |
5. Question Answering
Leader Board
| Research Paper | Datasets | Metric | Source Code | Year |
|---|---|---|---|---|
| BERT-LARGE (ensemble) | The Stanford Question Answering Dataset |
| 2018 |
6. Named entity recognition
| Research Paper | Datasets | Metric | Source Code | Year |
|---|---|---|---|---|
| Named Entity Recognition in Twitter using Images and Text | Ritter |
| NOT FOUND | 2017 |
7. Abstractive Summarization
| Research Paper | Datasets | Metric | Source Code | Year |
|---|---|---|---|---|
| Cutting-off redundant repeating generations for neural abstractive summarization |
|
| NOT YET AVAILABLE | 2017 |
| Convolutional Sequence to Sequence |
|
| PyTorch | 2017 |
8. Dependency Parsing
| Research Paper | Datasets | Metric | Source Code | Year |
|---|---|---|---|---|
| Globally Normalized Transition-Based Neural Networks |
|
|
|
Computer Vision
1. Classification
| Research Paper | Datasets | Metric | Source Code | Year |
|---|---|---|---|---|
| Dynamic Routing Between Capsules |
|
| 2017 | |
| High-Performance Neural Networks for Visual Object Classification |
|
| 2011 | |
| Giant AmoebaNet with GPipe |
|
| 2018 | |
| ShakeDrop regularization |
|
| 2017 | |
| Aggregated Residual Transformations for Deep Neural Networks |
|
| 2017 | |
| Random Erasing Data Augmentation |
|
| Pytorch | 2017 |
| EraseReLU: A Simple Way to Ease the Training of Deep Convolution Neural Networks |
|
| Pytorch | 2017 |
| Dynamic Routing Between Capsules |
|
| 2017 | |
| Learning Transferable Architectures for Scalable Image Recognition |
|
| 2017 | |
| Squeeze-and-Excitation Networks |
|
| 2017 | |
| Aggregated Residual Transformations for Deep Neural Networks |
|
| 2016 |
2. Instance Segmentation
| Research Paper | Datasets | Metric | Source Code | Year |
|---|---|---|---|---|
| Mask R-CNN |
|
| 2017 |
3. Visual Question Answering
| Research Paper | Datasets | Metric | Source Code | Year |
|---|---|---|---|---|
| Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge |
|
| 2017 |
4. Person Re-identification
| Research Paper | Datasets | Metric | Source Code | Year |
|---|---|---|---|---|
| Random Erasing Data Augmentation |
| Pytorch | 2017 |
Speech
1. ASR
| Research Paper | Datasets | Metric | Source Code | Year |
|---|---|---|---|---|
| The Microsoft 2017 Conversational Speech Recognition System |
|
| 2017 | |
| The CAPIO 2017 Conversational Speech Recognition System |
|
| 2017 |
Semi-supervised Learning
Computer Vision
| Research Paper | Datasets | Metric | Source Code | Year |
|---|---|---|---|---|
| DISTRIBUTIONAL SMOOTHINGWITH VIRTUAL ADVERSARIAL TRAINING |
|
| Theano | 2016 |
| Virtual Adversarial Training: a Regularization Method for Supervised and Semi-supervised Learning |
|
| 2017 | |
| Few Shot Object Detection |
|
| 2017 | |
| Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro |
|
| 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 |
|
| 2017 | |
| Unsupervised Neural Machine Translation with Weight Sharing |
|
| 2018 |
Transfer Learning
| Research Paper | Datasets | Metric | Source Code | Year |
|---|---|---|---|---|
| One Model To Learn Them All |
|
| 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