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
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
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
##Datasets