2013 NIPS Proceedings – Advances in Neural Information Processing Systems
papers.nips.cc
papers.nips.cc
http://cs.stanford.edu/people/karpathy/nips2013/
The page allows you to toggle LDA topics and off to browse the papers, or (my personal favorite) find a paper you like and sort the other papers according to tf-idf similarity, which tends to reveal exceptionally relevant papers.
I've been sifting through these papers trying to prioritize by to relevance to my work so that I can get them into mendeley and dig in. You just made it a lot easier. Looks like a pretty good haul this year, for me :-)
Would be great to have this similarity-based navigation on http://openreview.net as well.
I'd originally submitted this earlier today, with the headline "Neural Information Processing Systems (NIPS) 2013 Proceedings": https://news.ycombinator.com/item?id=6815771
It only got one other upvote and never made it to the front page. Meanwhile, another article submitted at almost exactly the same time with far less interesting content but a more provocative headline (on a user getting banned from Uber for API abuse) got ~15 votes, pushing it onto the front page.
I re-submitted this page with a slightly more descriptive (and buzzwordy) headline ("State of the art Machine Learning papers: NIPS 2013"), and it almost immediately ended up on the front page. Since then the headline has been reverted to the page's headline, again slowing the rate at which it's received votes.
Your first post did not hit HN's RSS feed (IIRC, at least 3 votes are needed for the vanilla feed), otherwise I would've upvoted it. This one did.
[1]: http://atpassos.me/post/67560831508/nips-2013-reading-list.
More difficult : only by reading titles.
An important activity of the researcher is to sort between interesting papers and garbage, since the selection process of even high level conference is deeply broken.
Just read SIGIR proceedings where every paper beats the previous baseline by 0.X % on datasets that do not represent the real problem, it's just an example among many others.
Also check this interesting analysis where the authors analyse best vs top cited papers over a span of ten years: http://arnetminer.org/conferencebestpapers
Here you can see that some conferences where able to identify lasting value and others not.
I said that the motivations behind academics to publish lead to the publishing of tons of papers that while being scientifically correct (at least for top-tier conferences) bring absolutely nothing to the party.
http://icml.cc/2013/?page_id=47 (click on the schedule images)
Don't believe anyone's done this for NIPS 2013 yet.
In a few days, ICCV 2013 papers will be published too: http://www.cv-foundation.org/openaccess/ICCV2013.py