Three Highly Technical Reference Pages: Reinforcement Learning, Deep Vision, Recurrent Neural Networks and state of the art page in object classification

 

 

Jiwon Kim is at it again as he builds, with whoever wants to help, several curated page of highly technical references pages on the following topics, they are:

• Awesome Reinforcement Learning by Hyunsoo Kim, Jiwon Kim
• Awesome Deep Vision by Jiwon Kim, Heesoo Myeong, Myungsub Choi, Jung Kwon Lee, Taeksoo Kim
• Awesome Recurrent Neural Networks by Myungsub Choi, Jiwon Kim, Taeksoo Kim 

 

Obviously, they have been added to The List.

 

I am also going to be adding the current state of the art in objects classification by Rodrigo Benenson. 

h/t Desernaut

 Credit: ESA/Rosetta/MPS for OSIRIS Team MPS/UPD/LAM/IAA/SSO/INTA/UPM/DASP/IDA

 

OSIRIS narrow-angle camera image taken on 3 August 2016, when Rosetta was 8.7 km from the centre of Comet 67P/Churyumov–Gerasimenko. The scale is 0.15 m/pixel at the comet and the image measures about 307 m.

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Highly Technical Reference Page: Laplacian Linear Equations, Graph Sparsification, Local Clustering, Low-Stretch Trees, etc. + implementation

Here is a new Highly Technical Reference Page entitled Laplacian Linear Equations, Graph Sparsification, Local Clustering, Low-Stretch Trees, etc. by Dan Spielman. Thanks to Rich Seymour"s tweet, here is a release of  Laplacians.jl a package built by Dan and collaborators. From the page:

Laplacians is a package containing graph algorithms, with an emphasis on tasks related to spectral and algebraic graph theory. It contains (and will contain more) code for solving systems of linear equations in graph Laplacians, low stretch spanning trees, sparsifiation, clustering, local clustering, and optimization on graphs.
All graphs are represented by sparse adjacency matrices. This is both for speed, and because our main concerns are algebraic tasks. It does not handle dynamic graphs. It would be very slow to implement dynamic graphs this way.
The documentation may be found in http://danspielman.github.io/Laplacians.jl/about/index.html.
This includes instructions for installing Julia, and some tips for how to start using it. It also includes guidelines for Dan Spielman's collaborators.
For some examples of some of the things you can do with Laplacians, look at

• this Julia notebook.
• [Low Stretch Spanning Trees](http://danspielman.github.io/Laplacians.jl/LSST/index.html
• Information about solving Laplacian equations
• And, try the chimera and wtedChimera graph generators. They are designed to generate a wide variety of graphs so as to exercise code.

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The Sparse- and Low-Rank Approximation Wiki

I was recently asked about a place on the interweb where people talked about solvers used in compressive sensing and more. That place exists, it is the wiki of Stephen Becker called Sparse- and low-rank approximation wiki

It is one of the few Highly Technical Reference Pages / Aggregators on the interweb.

 

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Highly Technical Aggregators - request -

Following up on an earlier mention by Muthu Muthukrishnan of the need to find aggregators, I went ahead and created a list of reference pages on advanced subjects curated by specialists in fast evolving areas. This list can be bookmarked as it is here: http://nuit-blanche.blogspot.com/p/reference-page.html

So far, here is the listing I have. If you know of any similar reference pages, please let me know and I'll add it here:

• List of Open Problems in Sublinear Algorithms 
• Superlinear Indexes maintained by the following people
• Count-Min Sketches and Applications: maintained by Muthu Muthukrishnan and Andrew McGregor
• Resource of Quantization maintained by Petros Boufounos
• 1 bit Compressive Sensing maintained by Jason Laska (Rice)
• The Compressive Sensing Resource at Rice University maintained by the group of Rich Baraniuk
• Compressive Sensing: The Big Picture maintained by Igor Carron
• Compressive Sensing Hardware
• Advanced Matrix Factorization Jungle maintained by Igor Carron
• The sparse- and low-rank solver wiki: maintained by Stephen Becker
• Nonlinear Compressive Sensing by Yonina Eldar, Allen Yang, Roy Dong and Henrik Ohlsson
• The LASSO page by Rob Tibshirani 
• Recommender Systems: wiki maintained by anyone who wishes to contribute
• Submodularity maintained by Andreas Krause and Carlos Guestrin
• Deep Learning
• WIKIMIZATION, a repository and resource for all things Optimization. by Jon Dattorro
• History of Approximation Theory (HAT) by Carl de Boor, Kirill Kopotun and Allan Pinku
• AdExchange by Muthu Muthukrishnan 

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Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.