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 If you do find a problem's SoTA result is out of date or missing, please raise this as an issue (with this information: research paper name, dataset, metric, source code and year).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.
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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
Research Paper | Datasets | Metric | Source Code | Year |
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DYNAMIC EVALUATION OF NEURAL SEQUENCE MODELS |
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Pytorch | 2017 |
Averaged Stochastic Gradient Descent with Weight Dropped LSTM or QRNN |
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Pytorch | 2017 |
FRATERNAL DROPOUT |
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Pytorch | 2017 |
Factorization tricks for LSTM networks | One Billion Word Benchmark | Preplexity: 23.36 | Tensorflow | 2017 |
Research Paper | Datasets | Metric | Source Code | Year |
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Attention Is All You Need |
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2017 | |
NON-AUTOREGRESSIVE NEURAL MACHINE TRANSLATION |
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NOT YET RELEASED | 2017 |
Research Paper | Datasets | Metric | Source Code | Year |
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Learning Structured Text Representations | Yelp | Accuracy: 68.6 | NOT YET AVAILABLE | 2017 |
Attentive Convolution | Yelp | Accuracy: 67.36 | NOT YET AVAILABLE | 2017 |
Research Paper | Datasets | Metric | Source Code | Year |
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DiSAN: Directional Self-Attention Network for RNN/CNN-free Language Understanding |
Stanford Natural Language Inference (SNLI) | Accuracy: 51.72 | NOT YET AVAILABLE | 2017 |
Research Paper | Datasets | Metric | Source Code | Year |
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Interactive AoA Reader+ (ensemble) | The Stanford Question Answering Dataset |
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NOT YET AVAILABLE | 2017 |
Research Paper | Datasets | Metric | Source Code | Year |
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Named Entity Recognition in Twitter using Images and Text |
Ritter | F-measure: 0.59 | NOT YET AVAILABLE | 2017 |
Research Paper | Datasets | Metric | Source Code | Year |
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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 | NOT FOUND | 2011 |
Dynamic Routing Between Capsules | CIFAR-10 | Test Error: 10.6% | 2017 | |
Dynamic Routing Between Capsules | MultiMNIST | Test Error: 5% | 2017 |
Research Paper | Datasets | Metric | Source Code | Year |
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The Microsoft 2017 Conversational Speech Recognition System | Switchboard Hub5'00 | WER: 5.1 | NOT FOUND | 2017 |
Research Paper | Datasets | Metric | Source Code | Year |
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DISTRIBUTIONAL SMOOTHINGWITH VIRTUAL ADVERSARIAL TRAINING |
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Theano | 2016 |
Virtual Adversarial Training: a Regularization Method for Supervised and Semi-supervised Learning |
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NOT FOUND | 2017 |
Research Paper | Datasets | Metric | Source Code | Year |
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PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION | Unsupervised CIFAR 10 | Inception score: 8.80 | Theano | 2017 |
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