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 If you do find a problem's SoTA result is out of date or missing, please raise this as an issue (with this information: research paper name, dataset, metric, source code and year).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