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 If you do find a problem’s SoTA result is out of date or missing, please raise this as an issue or submit Google form (with this information: research paper name, dataset, metric, source code and year).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

repo name	RedditSota/state-of-the-art-result-for-machine-learning-problems
repo link	https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems
homepage
language
size (curr.)	151 kB
stars (curr.)	8929
created	2017-11-09
license	Apache License 2.0

State-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 If you do find a problem’s SoTA result is out of date or missing, please raise this as an issue (with this information: research paper name, dataset, metric, source code and year).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
  • Speech
  • Computer Vision
  • NLP
• Semi-supervised Learning
  • Computer Vision
• Unsupervised Learning
  • Speech
  • Computer Vision
  • NLP
• Transfer Learning
• Reinforcement Learning

Supervised Learning

NLP

1. Language Modelling

2. Machine Translation

3. Text Classification

4. Natural Language Inference

Leader board:

Stanford Natural Language Inference (SNLI)

MultiNLI

5. Question Answering

Leader Board

SQuAD

6. Named entity recognition

7. Abstractive Summarization

Research Paper	Datasets	Metric	Source Code	Year
Cutting-off redundant repeating generations for neural abstractive summarization	DUC-2004Gigaword	DUC-2004 ROUGE-1: 32.28 ROUGE-2: 10.54 ROUGE-L: 27.80 Gigaword ROUGE-1: 36.30 ROUGE-2: 17.31 ROUGE-L: 33.88	NOT YET AVAILABLE	2017
Convolutional Sequence to Sequence	DUC-2004Gigaword	DUC-2004 ROUGE-1: 33.44 ROUGE-2: 10.84 ROUGE-L: 26.90 Gigaword ROUGE-1: 35.88 ROUGE-2: 27.48 ROUGE-L: 33.29	PyTorch	2017

8. Dependency Parsing

Research Paper	Datasets	Metric	Source Code	Year
Globally Normalized Transition-Based Neural Networks	Final CoNLL ’09 dependency parsing	94.08% UAS accurancy 92.15% LAS accurancy	SyntaxNet	2017

Computer Vision

1. Classification

2. Instance Segmentation

3. Visual Question Answering

4. Person Re-identification

Speech

Speech SOTA

1. ASR

Semi-supervised Learning

Computer Vision

Unsupervised Learning

Computer Vision

1. Generative Model

NLP

Machine Translation

Transfer Learning

Reinforcement Learning

Email: yxt.stoaml@gmail.com