https://github.com/m0nologuer/AI-reading-list

Up to date list of the most interesting papers in AI
https://github.com/m0nologuer/AI-reading-list

Last synced: about 2 months ago
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Up to date list of the most interesting papers in AI

• Host: GitHub
• URL: https://github.com/m0nologuer/AI-reading-list
• Owner: m0nologuer
• Created: 2015-11-30T03:30:35.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2015-11-30T11:51:55.000Z (almost 9 years ago)
• Last Synced: 2024-05-16T12:29:50.676Z (4 months ago)
• Size: 2.93 KB
• Stars: 136
• Watchers: 14
• Forks: 27
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# AI-reading-list

This is my personal  list of current AI papers I'm reading/ have yet to read. Just things I think point in interesting directions, or topics I'm interested in. 

## General

[Tensorflow](http://download.tensorflow.org/paper/whitepaper2015.pdf) - Google's large scale infrastructure project

[Representation learning](http://arxiv.org/abs/1206.5538) - survey paper on representation methods

[Adversarial Networks](http://arxiv.org/abs/1406.2661) - framework for generation

[Neural Turing Machine](http://arxiv.org/abs/1410.5401)

## RNN structures 

[LTSM](http://web.eecs.utk.edu/~itamar/courses/ECE-692/Bobby_paper1.pdf) - long term short term memory

[Memory Networks](http://arxiv.org/abs/1410.3916/) - on adding memory storage

[End to End Memory networks](http://arxiv.org/abs/1503.08895) - Facebook's memory storage

[Neural Programmer](http://arxiv.org/abs/1511.04834) - on adding basic artithmetic operations

[Spatial Transformer](http://arxiv.org/abs/1509.05329) - DeepMind digit classification

[Deep Speech](http://arxiv.org/abs/1412.5567) - speech implementation

## Word Vectors

[word2vec](http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf) - on creating vectors to represent language, useful for RNN inputs

[sense2vec](http://arxiv.org/abs/1511.06388) - on word sense disambiguation

[Infinite Dimensional Word Embeddings](http://arxiv.org/abs/1511.05392) - new

[Skip Thought Vectors](http://arxiv.org/abs/1506.06726) - word representation method

[Adaptive skip-gram](http://arxiv.org/abs/1502.07257) - similar approach, with adaptive properties

## Natural Language

[Neural autocoder for paragraphs and documents](http://arxiv.org/abs/1506.01057) - LTSM representation

[LTSM over tree structures](http://arxiv.org/abs/1503.04881)

[Sequence to Sequence Learning](http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf) - word vectors for machine translation

[Teaching Machines to Read and Comprehend](http://arxiv.org/abs/1506.03340) - DeepMind paper

## Convolutional neural nets

[DRAW](http://jmlr.org/proceedings/papers/v37/gregor15.pdf)- An RNN for image classfication

[ImageNet Classification](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) - popular paper

[A Neural Algorithm of Artistic Style](http://arxiv.org/pdf/1508.06576v1.pdf) - popular papeer

[Generative Adversarial Networks](http://arxiv.org/abs/1511.06434) - unsupervised learning to generate images

##Tutorials

[LTSM RNN in Python](http://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/)

[Tensorflow Tutorials](https://github.com/nlintz/TensorFlow-Tutorials)

[K-Means with Tensorflow](https://codesachin.wordpress.com/2015/11/14/k-means-clustering-with-tensorflow/)

##Datasets

[DeepMind Q&A Corpus](https://github.com/deepmind/rc-data/)