Friday, December 20, 2013

#NIPS2013 sweet aftertastes


So NIPS2013 is finished, let's see what was interesting and picked by others:

Blog coverage:

I also found these links of interest

As well as well as these tweets and their interesting links suggested on the #NIPS2013 tag on twitter (here is google docs list of all the tweets):

Animesh Garg ‏@Animesh_Garg

Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics ProgramsVikash K. Mansinghka, Tejas D. Kulkarni, Yura N. Perov, Joshua B. Tenenbaum


Il Memming Park ‏@memming
A simple example of Dirichlet process mixture inconsistency for the number of components[PDF]

Suvash Sedhain ‏@suvsh
Deep content based recommendation #NIPS2013 http://media.nips.cc/nipsbooks/nipspapers/paper_files/nips26/1239.pdf … and my answer in Quora


brendan o'connor ‏@brendan64
since i keep telling people at #nips2013 about @redpony's notes on adagrad, here they are, they are handy => http://www.ark.cs.cmu.edu/cdyer/adagrad.pdf …


Notes on AdaGrad by Chris Dyer

Andreas Mueller ‏@t3kcit
Great tutorial by Rob Fergus on Deep Learning for Vision at #NIPS2013. Slides up soon, for now read their paper: http://arxiv.org/pdf/1311.2901v3.pdf …

brendan o'connor ‏@brendan64
#NIPS2013 I really love Pearl's 2009 review http://ftp.cs.ucla.edu/pub/stat_ser/r350.pdf … I imagine this tutorial is hard to understand w/o reading that first ...



.@brendan642 #NIPS2013 
.. also good are Cosma Shalizi's book chapters: http://www.stat.cmu.edu/~cshalizi/uADA/12/lectures/ch22.pdf … http://www.stat.cmu.edu/~cshalizi/uADA/12/lectures/ch23.pdf … http://www.stat.cmu.edu/~cshalizi/uADA/12/lectures/ch24.pdf …

Shane Conway ‏@statalgo
"Approximate Dynamic Programming Finally Performs Well in the Game of Tetris" http://media.nips.cc/nipsbooks/nipspapers/paper_files/nips26/881.pdf … #ADP #NIPS2013


Alexandre Passos ‏@atpassos_ml
New blog post: #NIPS2013 reading list http://atpassos.me/post/67560831508/nips-2013-reading-list …

Gilles Louppe ‏@glouppe
My step for #OpenScience: code+demo for our #NIPS2013 paper "Understanding variable importances in random forests" https://github.com/glouppe/paper-variable-importances …

Animesh Garg ‏@Animesh_Garg
Distributed Submodular Maximization: Simple effective and very few assumptions on functions win-win #NIPS2013 http://bit.ly/1gNBNVb

Richard ‏@RichardSocher

Demo of our website to make machine learning for text classification easily accessible: http://etcml.com at #NIPS2013

Gilles Louppe ‏@glouppe

Just presented "Scikit-Learn: Machine Learning in the Python ecosystem" at MLOSS #NIPS2013 Find the notebook at http://nbviewer.ipython.org/github/glouppe/talk-sklearn-mloss-nips2013/blob/master/oral/sklearn-mloss.ipynb …


Andreas Mueller ‏@t3kcit
ClowdFlows looks like a great way to design data pipelines and do interactive exploration via the web http://www.clowdflows.org/ #NIPS2013


Olivier Grisel ‏@ogrisel

Decision Jungles: memory efficient alternative to randomized trees http://papers.nips.cc/paper/5199-decision-jungles-compact-and-rich-models-for-classification … /cc @glouppe @pprett via @Chris_Said #NIPS2013

Andreas Mueller ‏@t3kcit

As a consequence of #NIPS2013 I'm finally installing pylearn2 http://deeplearning.net/software/pylearn2/ … Probably the best way to get started with deep learning


Chandra ‏@sekhardrona

Deep learning algorithms could make smart drugs http://goo.gl/Mm2Qq5 via @physorg_com #NIPS2013 #MachineLearning


Dave Sullivan ‏@_DaveSullivan


FastML: 13 #NIPS2013 papers that caught our eye http://fastml.com/13-nips-papers-that-caught-our-eye/ …

Dirk Gorissen ‏@elazungu

Interesting paper from #NIPS2013 "able to predict >95% of the weights of a deep NN without any drop in accuracy" http://media.nips.cc/nipsbooks/nipspapers/paper_files/nips26/1053.pdf …


Adam Stankiewicz ‏@astankiew

NIPS 2013 Workshop on Data Driven Education http://lytics.stanford.edu/datadriveneducation/index.html#panel … #nips2013 via @jonathanhuang11

eliana feasley ‏@eli_awry

Announced KA data sharing plans at #NIPS2013 . We're working on a whitepaper with more details, but initial info at http://khanacademy.org/r/research


Chandra ‏@sekhardrona

explain my data: NIPS and the Zuckerberg Visit http://goo.gl/CrRbif #NIPS2013 #DeepLearning

Michael Witbrock ‏@witbrock
My latest : Cyc and Semantic Construction Grammar #NIPS2013… on @slideshare http://www.slideshare.net/witbrock/cyc-and-semantic-construction-grammar-nips-2013-ket-workshop … via @SlideShare

Tim van Erven ‏@tverven

Blog post: Wrote a PAC-Bayes mini-tutorial on the plane to #NIPS2013 to relate to standard concentration inequalities http://www.timvanerven.nl/blog/2013/12/pac-bayes-mini-tutorial-a-continuous-union-bound/ …
joseph reisinger ‏@josephreisinger
"Machine learning is a complex ecosystem"— Max Welling on the whole "Zuck at #nips2013" thing http://scientificpearlsofwisdom.blogspot.com/2013/12/i-was-conference-chair-for-nips-2013.html …

Erin LeDell ‏@ledell
Now anyone that can query a database can do Bayesian inference http://probcomp.csail.mit.edu/bayesdb/index.html … #BayesDB
Dropout (neural networks/Geoffrey Hinton) as adaptive regularization in GLMs #NIPS2013 http://media.nips.cc/nipsbooks/nipspapers/paper_files/nips26/246.pdf …

Erin LeDell ‏@ledel
Compressive feature learning can reduce text feature space by two orders of magnitude compared to k-grams #NIPS2013 http://media.nips.cc/nipsbooks/nipspapers/paper_files/nips26/1342.pdf …









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