I've been trying to read a paper a day since midsummer. These are a few of the interesting, for me personally, since then: Generating Sequences With Recurrent

I've been trying to read a paper a day since midsummer. These are a few of the interesting, for me personally, since then: Generating Sequences With Recurrent Neural Networks - http://arxiv.org/abs/1308.0850 http://arxiv.org/abs/1308.0850 Older one, but important to understand deeply since other recent ideas have come from this! Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks - http://arxiv.org/abs/1511.06434 http://arxiv.org/abs/1511.06434 Unitary Evolution Recurrent Neural Networks - http://arxiv.org/abs/1511.06464 http://arxiv.org/abs/1511.06464 State of the Art Control of Atari Games Using Shallow Reinforcement Learning - http://arxiv.org/abs/1512.01563 http://arxiv.org/abs/1512.01563 Interesting discussion in section 6.1 on the shortcomings/issues of DQN done by Deepmind Spectral Representations for Convolutional Neural Networks - http://arxiv.org/abs/1506.03767 http://arxiv.org/abs/1506.03767 Deep Residual Learning for Image Recognition - http://arxiv.org/abs/1512.03385 http://arxiv.org/abs/1512.03385 Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) - http://arxiv.org/abs/1511.07289 http://arxiv.org/abs/1511.07289 I wish they did more comparisons between similar network architecture with only the units swapped out. Eg. Alexnet, Relu vs Alexnet, Elu. On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models - http://arxiv.org/abs/1511.09249 http://arxiv.org/abs/1511.09249 Just a few from my list :)