Ask HN: Papers you read in 2015?

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 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

Unitary Evolution Recurrent Neural Networks - http://arxiv.org/abs/1511.06464

State of the Art Control of Atari Games Using Shallow Reinforcement Learning - 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

Deep Residual Learning for Image Recognition - http://arxiv.org/abs/1512.03385

Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) - 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

Just a few from my list :)

What is a good resource for someone who want to read good papers from time to time?

A decent resource: http://paperswelove.org/

Surprised nobody mentioned The Morning Paper yet: http://blog.acolyer.org/

Generalized LL Parsing - http://dotat.at/tmp/gll.pdf

Parse ambiguous context-free grammars in worst-case cubic time and unambiguous grammars in linear time, with an intuitive recursive-descent-ish algorithm. GLL is the future of parsing IMO, more powerful than packrat/PEG parsers and comparatively easy to write by hand. It also handles ambiguities more elegantly than GLR, IMO.

Dependency-Based Word Embeddings - https://levyomer.files.wordpress.com/2014/04/dependency-base...

word2vec algorithm with context based on linguistic dependencies instead of a skip-gram approach. A quick explanation is skip-grams give words related to the embedding (ex: Hogwarts -> Dumbledore) and dependencies give words that can be used like the embedding (ex: Hogwards -> Sunnydale). It's not meant to replace skip-grams, but augment them; skip-gram contexts learn the domain and dependency-based contexts learn the semantic type.