/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 If you do find a problem's SoTA result is out of date or missing, please raise this as an issue or submit Google form (with this information: research paper name, dataset, metric, source code and year).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.

Apache License 2.0Apache-2.0

State-of-the-art result for all Machine Learning Problems

LAST UPDATE: 10th November 2017

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: redditsota@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 If you do find a problem's SoTA result is out of date or missing, please raise this as an issue (with this information: research paper name, dataset, metric, source code and year).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

NLP

1. Language Modelling

Research Paper Datasets Metric Source Code Year
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
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

Leader board:

Stanford Natural Language Inference (SNLI)

MultiNLI

Research Paper Datasets Metric Source Code Year
NATURAL LANGUAGE INFERENCE OVER INTERACTION SPACE Stanford Natural Language Inference (SNLI) Accuracy: 88.9 Tensorflow 2017

5. Question Answering

Leader Board

SQuAD

Research Paper Datasets Metric Source Code Year
Interactive AoA Reader+ (ensemble) The Stanford Question Answering Dataset
  • Exact Match: 79.083
  • F1: 86.450
NOT YET AVAILABLE 2017

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 YET AVAILABLE 2017

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 NOT FOUND 2011
Aggregated Residual Transformations for Deep Neural Networks CIFAR-10 Test Error: 3.58% 2016
Dynamic Routing Between Capsules MultiMNIST Test Error: 5% 2017
Aggregated Residual Transformations for Deep Neural Networks ImageNet-1k Top-1 Error: 20.4% 2016

Speech

1. ASR

Research Paper Datasets Metric Source Code Year
The Microsoft 2017 Conversational Speech Recognition System 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

NLP

Machine Translation

Research Paper Datasets Metric Source Code Year
UNSUPERVISED MACHINE TRANSLATION USING MONOLINGUAL CORPORA ONLY
  • WMT16 (en-fr fr-en de-en en-de)
  • Multi30k-Task1(en-fr fr-en de-en en-de)
  • BLEU:(32.76 32.07 26.26 22.74)
  • BLEU:(15.05 14.31 13.33 9.64)
NOT YET RELEASED 2017
## Transfer Learning

Reinforcement Learning

Email: redditsota@gmail.com