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.

Tensorflow - Google's large scale infrastructure project

Representation learning - survey paper on representation methods

Adversarial Networks - framework for generation

Neural Turing Machine

LTSM - long term short term memory

Memory Networks - on adding memory storage

End to End Memory networks - Facebook's memory storage

Neural Programmer - on adding basic artithmetic operations

Spatial Transformer - DeepMind digit classification

Deep Speech - speech implementation

word2vec - on creating vectors to represent language, useful for RNN inputs

sense2vec - on word sense disambiguation

Infinite Dimensional Word Embeddings - new

Skip Thought Vectors - word representation method

Adaptive skip-gram - similar approach, with adaptive properties

Neural autocoder for paragraphs and documents - LTSM representation

LTSM over tree structures

Sequence to Sequence Learning - word vectors for machine translation

Teaching Machines to Read and Comprehend - DeepMind paper

DRAW- An RNN for image classfication

ImageNet Classification - popular paper

A Neural Algorithm of Artistic Style - popular papeer

Generative Adversarial Networks - unsupervised learning to generate images

##Tutorials LTSM RNN in Python

Tensorflow Tutorials

K-Means with Tensorflow

##Datasets

DeepMind Q&A Corpus