

Unsupervised Feature Learning and Deep Learning Recommended Readings - jcr
http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Recommended_Readings

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benanne
There's some great stuff there, but that list could use an update. The most
recent papers seem to be from 2011. Things move very fast in this field. This
was back when pre-training was still strongly recommended and before
Krizhevsky's ImageNet win (2012), which shifted the focus of the field away
from unsupervised learning somewhat. The section on convolutional neural
networks especially is a bit meager by today's standards :)

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jcr
You made some really great points, but if Geoffrey Hintons's list of "relevant
literature" has nothing newer than 2007, then, it seems nobody has solved the
continuous update problem for fast moving fields. ;)

[http://www.cs.toronto.edu/~hinton/deeprefs.html](http://www.cs.toronto.edu/~hinton/deeprefs.html)

A bit more seriously, the Stanford UFLDL wiki seems to be intended more as an
initial tutorial. It's related their CS classes so it provides recommended
reading on tutorial-relevant and class-relevant works. It may lack some of the
more recent advancements, but it still seems to be an active work in progress,
and it's received about 1000 edits in the last 90 days.

[http://deeplearning.stanford.edu/wiki/index.php/Special:Acti...](http://deeplearning.stanford.edu/wiki/index.php/Special:ActiveUsers)

It's Stanford and it's class/research related, so I'm not sure if the wiki is
open to public editing (I haven't tried yet). If you have ideas/links to
improve the reading list, I'm sure they'd appreciate either feedback or wiki
edits.

I think I managed to track down the Krizhevsky paper you mentioned?

[http://books.nips.cc/papers/files/nips25/NIPS2012_0534.pdf](http://books.nips.cc/papers/files/nips25/NIPS2012_0534.pdf)

    
    
      Krizhevsky, A., Sutskever, I. and Hinton, G. E.
      ImageNet Classification with Deep Convolutional Neural Networks
      NIPS 2012: Neural Information Processing Systems, Lake Tahoe, Nevada
    
    

Thanks for mentioning it.

As a useful resource, the Stanford UFLDL reading list still seemed worth
posting to HN. There are just mountains of AI/ML/DL/NN/... papers, projects
and related resources, so picking out interesting and useful things for HN can
be really difficult.

~~~
benanne
True, there is no real up to date curated reading list that I know of.
Actually, there was a list of 2014 deep learning papers going around recently,
but it seemed to list basically every tangentially related paper that the
authors could find, so it was pretty useless as an introduction to the field.

~~~
Houshalter
There are some survey papers like this:
[http://arxiv.org/abs/1404.7828](http://arxiv.org/abs/1404.7828)

The point isn't to read every paper it references, but to just get a summary
of what the current state of research is. If you are interested in an area
then you can go read the papers there.

------
jcr
There are some related handouts and lecture videos here:

[http://web.stanford.edu/class/cs294a/handouts.html](http://web.stanford.edu/class/cs294a/handouts.html)

