Papers
My Machine Learning & Deep Learning Papers Notes.git
Contents
Machine Learning
Deep Learning
- Understanding the difficulty of training deep feedforward neural networks (2010)
- On the importance of initialization and momentum in deep learning (2013)
- Accelerating learning via knowledge transfer (2016)(Net2net)
Computer Vision
Natural Language Processing
- A Primer on Neural Network Models for Natural Language Processing
- Natural Language Processing (Almost) from Scratch
- Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing (2012)
- Distributed representations of words and phrases and their compositionality (2013)(word2vec)
- Efficient estimation of word representations in vector space (2013)
- Distributed representations of sentences and documents (2014)
- Glove: Global vectors for word representation (2014)
- Convolutional neural networks for sentence classification (2014)
- A convolutional neural network for modeling sentences (2014)
- Recursive deep models for semantic compositionality over a sentiment treebank (2013)
- Sequence to sequence learning with neural networks (2014)
- Generating sequences with recurrent neural networks (2013)(LSTM, very nice generating result, show the power of RNN)
- Learning phrase representations using RNN encoder-decoder for statistical machine translation (2014)
- Sequence to sequence learning with neural networks (2014)(Outstanding Work)
- Ask Me Anything: Dynamic Memory Networks for Natural Language Processing (2015)
- Character-Aware Neural Language Models (2015)
- Teaching Machines to Read and Comprehend (2015)(CNN/DailyMail cloze style questions)
- Very Deep Convolutional Networks for Natural Language Processing (2016)(state-of-the-art in text classification)
- Bag of Tricks for Efficient Text Classification (2016)(slightly worse than state-of-the-art, but a lot faster)
License
This project is licensed under the terms of the MIT license.github
完整项目工程见 github:Machine Learning & Deep Learning Paper Notes,持续更新,欢迎你们一块儿研读论文。web