[nrmn]

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 :)

[sgt101]

Crumbs, it takes me about two weeks to get through a paper properly!

[wodenokoto]

I can't speak for parent, but I believe people who read a paper a day, don't try to understand it deeply enough to be able to start implementing whatever the paper talks about. Rather it is read to get an idea of the approach and what kind of results it will give and what kind of problems it can solve.

[smhx]

For people actively working full-time in the field, some of the papers which have simple but powerful ideas, reading the paper for 2 hours (or glancing at the key diagram / formula) is enough to implement it.

For example:

Deep Residual Learning for Image Recognition

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

[elcritch]

When I read most papers, it will be to do what you say. There are many papers which really aren't worth spending more than 20 minutes perusing since they just rehash or tweak something which was done previously. Unfortunately with the publish-or-perish mentality predominant in most of academia, I'd say this is getting far worse and likely to increase. Sometimes I wish there were a "goodreads" or "netflix" for scientists.

Now, a good paper I will read and grok the main importance in a few hours to the point I can implement the basics. But, a classic paper will be like a well-thumbed classic and might take years to fully grasp.