Graphlab Workshop on Large Scale Machine Learning 2013

You probably recall this entry on Why should you care about last year's GraphLab workshop, Well, the reasons you should care have not really changed that much, except that after the 300+ researchers from 100+ companies who made it a success last year, Danny Bickson and his crew are organizing a new workshop on July 1st in the Bay Area.

Registration is now open and if you register now with the   NuitBlanche discount code you'll get a 25% (30%) discount on your registration fee . The much preliminary agenda is here. I am on the program committee and if you want to be a sponsor of the meeting like these other folks, please let me know, I'll forward your interest to the organizers. Danny also tells me that this time, they will get some subcontractor to produce the videos of the talks. In the meantime, here are some related entries:

• GraphLab workshop presentations
• Predicting the Future: Randomness and Parsimony (Part 1 is at Predicting the Future: The Steamrollers)
• GraphLab and GraphChi group on LinkedIn

All the blog entries mentioning GraphLab on Nuit Blanche can be found at:

http://nuit-blanche.blogspot.com/search/label/graphlab There is also a LinkedIn group for GraphLab GraphChi 

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Nuit Blanche in Review (March 2013)

If someone can take images from the Mars Curiosity website and stitch them together to create a panorama and get noticed by the interwebs in the process, do you think someone who would take on the Curiosity Super-Resolution Challenge and release a photo that not even NASA can provide, would get worldwide recognition. I am thinking yes, but then again, I am biased. 

Anyway, without further due, here are the exciting entries that sprung up since the last Nuit Blanche in Review for February 2013. We had six implementations made available by their authors. Let us note that we are now reaching the number 3 in the number of Analysis Operator Learning algorithms made available and we have close to that number in the number of algorithms dedicated to Sparse Subspace Clustering. Let's put this in perspective, six years ago to this day, there were only 3 reconstruction solvers available (besides CVX) in the field of compressive sensing The list has grown since and continues this month with SPASH and an Improved SL0. Other algorithms permit Spectral Compressive Sensing with Polar Interpolation or evaluate k-support Norm Regularized Risk Minimization. 

There were only two Sunday morning insights this month, one on Matrix Factorizations and the Grammar of Life and the other one on How to spot a compressive sensing system: there we looked into the case of the Randomized MALDI TOF MS/MS systems. Some feedback tells me that we'll see some action on this front in the future. Every month, there is a thematic that's forming under the weight of several publications/preprints. This month was no different, we saw much interest in phase transitions [see phase transition below and Living on the edge], the ever advances in MRI which accounts for a substantial number of papers that are truly innovative [Blind compressive sensing dynamic MRI below ] because some use the detection capabilities of compressive sensing as opposed to just reconstruction [ Magnetic resonance fingerprinting ]. I also noted the similarity between Well Logging and Nanopore Sequencing (more on that later) and that sometimes a least squares solution solves a different problem (see Convenience clouds your mind ).We also had some wonderful feedback from the readers as well as insightful blog entries from others, a few workshop announcements, videos and slides as well as two jobs announcements. Voila!

All other Nuit Blanche Monthly Reviews are at: 

http://nuit-blanche.blogspot.com/search/label/NuitBlancheReview

Sunday Morning Insights

• Sunday Morning Insight: How to spot a compressive sensing system: the case of the Randomized MALDI TOF MS/MS system
• Sunday Morning Insight: Matrix Factorizations and the Grammar of Life

Phase transition 

• Living on the edge: A geometric theory of phase transitions in convex optimization
• The Space of Solutions of Coupled XORSAT Formulae
• Maximin Analysis of Message Passing Algorithms for Recovering Block Sparse Signals

MRI

• Blind compressive sensing dynamic MRI
• Magnetic resonance fingerprinting

Op-eds

• Convenience clouds your mind: Determining building interior structures using compressive sensing
• Of Well Logging and Nanopore Sequencing

Inplementations:

• GOAL: Analysis Operator Learning and Its Application to Image Reconstruction - implementation -
• SPASH: Sparse Shape Reconstruction - implementation -
• Compressive Sensing for Spread Spectrum Receivers, Spectral Compressive Sensing with Polar Interpolation - implementations 
• Improving Smoothed l0 Norm in Compressive Sensing Using Adaptive Parameter Selection - implementation -
• A Note on k-support Norm Regularized Risk Minimization -implementation -
• Inductive Sparse Subspace Clustering - implementation - and Greedy Feature Selection for Subspace Clustering

Blogs and Reader's Reviews

• Nuit Blanche Reader's Reviews: SPASH, R-Functions, Structure in Biology, Randomized Kaczmarz and SDD, AMP and GAMP
• A User's Guide to Compressed Sensing for Communications Systems
• Around the blogs in 78 hours
• Around the blogs in 78 hours

Workshop announcements, videos and slides:

• GlobalSIP 2013 Symposium on: Low-Dimensional Models and Optimization in Signal Processing
• Videos: 2012 IPAM Graduate Summer School: Deep Learning, Feature Learning
• Videos: Asymptotics of Large-Scale Interacting Networks, Workshop Videos
• Slides of the Workshop on Sparse Fourier Transform Etc.
• From compression to compressed sensing
• Graphlab Workshop on Large Scale Machine Learning 2013

Jobs:

• Post-doctoral fellowship at Harvard Medical School: diffusion MRI and functional MRI
• MFJobs: Detecting genomic regions under Darwinian selection via sparse outlier detection techniques: a variational Bayes approach

Join the CompressiveSensing subreddit or the Google+ Community and post there !
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.

Videos: 2012 IPAM Graduate Summer School: Deep Learning, Feature Learning

From Yann LeCun's feed here are the videos of last summer's IPAM's grad school:

Graduate Summer School: Deep Learning, Feature Learning
July 9 - 27, 2012
UCLA Faculty Center 480 Charles E. Young Drive California Room
Printable Version
Monday, July 09, 2012
Morning Session
8:00 - 8:45	Check-In/Breakfast (Hosted by IPAM)
8:45 - 9:00	Welcome and Opening Remarks
9:00 - 10:00	Geoffrey Hinton (University of Toronto) "PART 1: Introduction to Deep Learning & Deep Belief Nets (PDF Parts 1 & 2)"  Presentation (PDF File) Play Video
10:00 - 10:30	Break
10:30 - 11:30	Geoffrey Hinton (University of Toronto) "PART 2: Using backpropagation for fine-tuning a generative model to be better at discrimination" Play Video
11:30 - 12:00	Break
12:00 - 1:00	Joint talk: James Bergstra (Harvard), Clement Farabet (NYU)
1:00 - 2:30	Lunch (on your own)
Afternoon Session
2:30 - 3:30	Yann LeCun (New York University) "Deep Learning, Graphical Models, Energy-Based Models, Structured Prediction" Play Video
3:30 - 4:00	Break
4:00 - 5:00	Yann LeCun (New York University) "Deep Learning, Graphical Models, EnergyBased Models, Structured Prediction (Part 2)" Presentation (PDF File) Play Video
5:00 - 6:30	Reception (Hosted by IPAM)
Tuesday, July 10, 2012
Morning Session
8:00 - 9:00	Continental Breakfast
9:00 - 10:00	Andrew Ng (Stanford University) "Deep Learning, Self-Taught Learning and Unsupervised Feature Learning (Part 1 Slides1-68; Part 2 Slides 69-109)" Presentation (PDF File) Play Video
10:00 - 10:30	Break
10:30 - 11:30	Andrew Ng (Stanford University) "Advanced topics + Research philosophy / Neural Networks: Representation" Presentation (PDF File) Play Video
11:30 - 12:00	Break
12:00 - 1:00	Andrew Ng (Stanford University) "Non-linear hypotheses" Play Video
1:00 - 2:30	Lunch (on your own)
Afternoon Session
2:30 - 3:30	Andrew Ng (Stanford University) "Non-linear hypotheses (Part 2)" Play Video
3:30 - 4:00	Break
4:00 - 5:00	Joint talk: James Bergstra (Harvard), Clement Farabet (NYU)
Wednesday, July 11, 2012
Morning Session
8:00 - 9:00	Continental Breakfast
9:00 - 10:00	Yann LeCun (New York University) "Learning Hierarchies of Invariant Features (Parts 1 & 2)" Presentation (PDF File) Play Video
10:00 - 10:30	Break
10:30 - 11:30	Yann LeCun (New York University) "Deep Learning, Graphical Models, EnergyBased Models, Structured Prediction (Part 3)" Play Video
11:30 - 12:00	Break
12:00 - 1:00	Joint talk: James Bergstra (Harvard), Clement Farabet (NYU)
1:00 - 2:30	Lunch (on your own)
Afternoon Session
2:30 - 3:30	Geoffrey Hinton (University of Toronto) "PART 3: Some applications of deep learning (Slides 1-38)" Presentation (PDF File) Play Video
3:30 - 4:00	Break
4:00 - 5:00	Geoffrey Hinton (University of Toronto) "PART 4: A computational principle that explains sex, the brain, and sparse coding (Slides 39-92)" Presentation (PDF File) Play Video
Thursday, July 12, 2012
Morning Session
8:00 - 9:00	Continental Breakfast
9:00 - 10:00	Rob Fergus (New York University) "Deep Learning Methods for Vision" Presentation (PDF File) Play Video
10:00 - 10:30	Break
10:30 - 11:30	Rob Fergus (New York University) "Deep Learning Methods for Vision (Part 2)" Play Video
11:30 - 12:00	Break
12:00 - 1:00	Geoffrey Hinton (University of Toronto)   Presentation (PDF File) Play Video
1:00 - 2:30	Lunch (on your own)
Afternoon Session
2:30 - 3:30	Alan Yuille (University of California, Los Angeles (UCLA)) "Part 1" Play Video
3:30 - 4:00	Break
4:00 - 5:00	Joint talk: James Bergstra (Harvard), Clement Farabet (NYU)
Friday, July 13, 2012
Morning Session
8:00 - 9:00	Continental Breakfast
9:00 - 10:00	Graham Taylor (University of Guelph)   Presentation (PDF File) Play Video
10:00 - 10:30	Break
10:30 - 11:30	Graham Taylor (University of Guelph)   Presentation (PDF File) Play Video
11:30 - 12:00	Break
12:00 - 1:00	Alan Yuille (University of California, Los Angeles (UCLA)) "Part 2" Play Video
1:00 - 2:30	Lunch (on your own)
Afternoon Session
2:30 - 3:30	Alan Yuille (University of California, Los Angeles (UCLA)) "Part 3" Play Video
3:30 - 4:00	Break
4:00 - 5:00	Yann LeCun (New York University) "Deep Learning, Graphical Models, EnergyBased Models, Structured Prediction (Part 4)" Play Video
Monday, July 16, 2012
Morning Session
8:00 - 9:00	Continental Breakfast
9:00 - 10:00	Yoshua Bengio (University of Montreal) "Part 1" Play Video
10:00 - 10:30	Break
10:30 - 11:30	Yoshua Bengio (University of Montreal) "Part 2" Play Video
11:30 - 12:00	Break
12:00 - 1:00	Stephen Wright (University of Wisconsin-Madison) "Some Relevant Topics in Optimization (Part 1) - (Slides Cover Parts 1 & 2)" Presentation (PDF File) Play Video
1:00 - 2:30	Lunch (on your own)
Afternoon Session
2:30 - 3:30	Stephen Wright (University of Wisconsin-Madison) "Some Relevant Topics in Optimization (Part 2)" Play Video
3:30 - 4:00	Break
4:00 - 5:00	Arthur Szlam (New York University) "A tutorial on sparse modeling." Presentation (PDF File) Play Video
5:00 - 6:30	Reception (Hosted by IPAM)
Tuesday, July 17, 2012
Morning Session
8:00 - 9:00	Continental Breakfast
9:00 - 10:00	Stephen Wright (University of Wisconsin-Madison) "Sparse and Regularized Optimization Part 1 (Slides Cover Parts 1 & 2)" Presentation (PDF File) Play Video
10:00 - 10:30	Break
10:30 - 11:30	Stephen Wright (University of Wisconsin-Madison) "Sparse and Regularized Optimization Part 2" Presentation (PDF File) Play Video
11:30 - 12:00	Break
12:00 - 1:00	Arthur Szlam (New York University)   Play Video
1:00 - 2:30	Lunch (on your own)
Afternoon Session
2:30 - 3:30	Yoshua Bengio (University of Montreal) "Part 3" Play Video
3:30 - 4:00	Break
4:00 - 5:00	Yoshua Bengio (University of Montreal) "Part 4" Play Video
Wednesday, July 18, 2012
Morning Session
8:00 - 9:00	Continental Breakfast
9:00 - 10:00	Stéphane Mallat (École Polytechnique) "Scattering Invariant Deep Networks for Classification " Presentation (PDF File) Play Video
10:00 - 10:30	Break
10:30 - 11:30	Stéphane Mallat (École Polytechnique) "Scattering Invariant Deep Networks for Classification (Part 2)" Play Video
11:30 - 12:00	Break
12:00 - 1:00	Kai Yu (Baidu Inc.) "Part 1" Play Video
1:00 - 2:30	Lunch (on your own)
Afternoon Session
2:30 - 3:30	Kai Yu (Baidu Inc.) "Part 2" Play Video
3:30 - 4:00	Break
4:00 - 5:00	Jorge Nocedal (Northwestern University) "Tutorial on Optimization methods for machine learning" Presentation (PDF File) Play Video
Thursday, July 19, 2012
Morning Session
8:00 - 9:00	Continental Breakfast
9:00 - 10:00	Jorge Nocedal (Northwestern University) "Tutorial on Optimization methods for machine learning (Part 2)" Play Video
10:00 - 10:30	Break
10:30 - 11:30	Jorge Nocedal (Northwestern University) "Tutorial on Optimization methods for machine learning (Part 3)" Play Video
11:30 - 2:00	Lunch (on your own)
Afternoon Session
2:00 - 3:30	Stéphane Mallat (École Polytechnique) "Scattering Invariant Deep Networks for Classification (Part 3)" Play Video
3:30 - 4:00	Break
4:00 - 5:00	Jason Morton (Pennsylvania State University) "An Algebraic Perspective on Deep Learning" Presentation (PDF File) Play Video
Friday, July 20, 2012
Morning Session
8:00 - 9:00	Continental Breakfast
9:00 - 10:00	Jason Morton (Pennsylvania State University) "An Algebraic Perspective on Deep Learning (Part 2)" Play Video
10:00 - 10:30	Break
10:30 - 11:30	Jason Morton (Pennsylvania State University) "An Algebraic Perspective on Deep Learning (Part 3)" Play Video
11:30 - 12:00	Break
12:00 - 1:00	Yoshua Bengio (University of Montreal)   Play Video
1:00 - 2:30	Lunch (on your own)
Afternoon Session
2:30 - 3:30	Stanley Osher (University of California, Los Angeles (UCLA))   Play Video
3:30 - 4:00	Break
4:00 - 5:00	Stanley Osher (University of California, Los Angeles (UCLA))   Play Video
Monday, July 23, 2012
Morning Session
8:00 - 9:00	Continental Breakfast
9:00 - 10:00	Ruslan Salakhutdinov (University of Toronto)   Presentation (PDF File) Play Video
10:00 - 10:30	Break
10:30 - 11:30	Ruslan Salakhutdinov (University of Toronto)   Presentation (PDF File) Play Video
11:30 - 12:00	Break
12:00 - 1:00	Marc'Aurelio Ranzato (Google Inc.) "Deep Gated MRF's - part 1" Presentation (PDF File) Play Video
1:00 - 2:30	Lunch (on your own)
Afternoon Session
2:30 - 3:30	Marc'Aurelio Ranzato (Google Inc.) "Deep Gated MRF's - part 2" Presentation (PDF File) Play Video
3:30 - 4:00	Break
4:00 - 5:00	Jason Weston (Google Research) "Part 1" Play Video
5:00 - 6:30	Reception (Hosted by IPAM)
6:30 - 7:30	Brian Milch (Google Inc.) "From Text to Concepts at Google"
Tuesday, July 24, 2012
Morning Session
8:00 - 9:00	Continental Breakfast
9:00 - 10:00	Jason Weston (Google Research) "Part 2" Play Video
10:00 - 10:30	Break
10:30 - 11:30	Jason Weston (Google Research) "Part 3" Play Video
11:30 - 12:00	Break
12:00 - 1:00	Ruslan Salakhutdinov (University of Toronto)   Presentation (PDF File) Play Video
1:00 - 2:30	Lunch (on your own)
Afternoon Session
2:30 - 3:30	Bruno Olshausen (University of California, Berkeley (UC Berkeley)) "From natural scene statistics to models of neural coding and representation (part 1)" Presentation (PDF File) Play Video
3:30 - 4:00	Break
4:00 - 5:00	Marc'Aurelio Ranzato (Google Inc.) "Large Scale Deep Learning" Presentation (PDF File) Play Video
Wednesday, July 25, 2012
Morning Session
8:00 - 9:00	Continental Breakfast
9:00 - 10:00	Thomas Serre (Brown University) "Deep learning in the visual cortex - Part 1" Presentation (PDF File) Play Video
10:00 - 10:30	Break
10:30 - 11:30	Thomas Serre (Brown University) "Deep learning in the visual cortex - Part 2" Presentation (PDF File) Play Video
11:30 - 12:00	Break
12:00 - 1:00	Roland Memisevic (Johann Wolfgang Goethe-Universität Frankfurt) "Multiview Feature Learning - Part 1" Presentation (PDF File) Play Video
1:00 - 2:30	Lunch (on your own)
Afternoon Session
2:30 - 3:30	Roland Memisevic (Johann Wolfgang Goethe-Universität Frankfurt) "Multiview Feature Learning - Part 2" Presentation (PDF File) Play Video
3:30 - 4:00	Break
4:00 - 5:00	Bruno Olshausen (University of California, Berkeley (UC Berkeley)) "From natural scene statistics to models of neural coding and representation (part 2)" Presentation (PDF File) Play Video
Thursday, July 26, 2012
Morning Session
8:00 - 9:00	Continental Breakfast
9:00 - 10:00	Iain Murray (University of Edinburgh) "Introduction to MCMC for deep learning" Presentation (PDF File) Play Video
10:00 - 10:30	Break
10:30 - 11:30	Iain Murray (University of Edinburgh) "Density estimation" Presentation (PDF File) Play Video
11:30 - 12:00	Break
12:00 - 1:00	Nando de Freitas (University of British Columbia) "Part 1" Play Video
1:00 - 2:30	Lunch (on your own)
Afternoon Session
2:30 - 3:30	Nando de Freitas (University of British Columbia) "Part 2" Play Video
3:30 - 4:00	Break
4:00 - 5:00	Thomas Serre (Brown University) "Deep learning in the visual cortex - Part 3" Presentation (PDF File) Play Video
Friday, July 27, 2012
Morning Session
8:00 - 9:00	Continental Breakfast
9:00 - 10:00	Nando de Freitas (University of British Columbia) "An Informal Mathematical Tour of Feature Learning" Presentation (PDF File) Play Video
10:00 - 10:30	Break
10:30 - 11:30	Nando de Freitas (University of British Columbia) "Part 3" Play Video
12:00 - 1:30	Lunch (on your own)
Afternoon Session
1:30 - 2:30	Panel Discussion: Nando de Freitas, Iain Murray, Bruno Olshausen, Ruslan Salakhutdinov, Roland Memisevic
2:30 - 3:00	Closing Remarks by Russ Caflisch (IPAM Director)

Join the CompressiveSensing subreddit or the Google+ Community and post there !
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.