It's been a year now and I think this monthly exercise is worth it for everybody, including me! At the same time, we have had Another Year in Reproducible Research in Compressive Sensing, Advanced Matrix Factorization and more featuring a slew of different implementations made available by their authors. As they say in Aggieland, it's the second time, so it is now officially a tradition: reproducible research is part of our DNA.
The last Nuit Blanche in review is here.
I just created the Paris Machine Learning Group on LinkedIn, if you are interested in our local meetups, or get a sense of what it entails being a machine learner in the city of lights, you might want consider joining. The next meetup will take place on October 16th and will feature probably none other than the awesome Andrew Gelman. If you are in the Paris area or traveling to Paris for a few days and want to talk about getting data and making sense of them (and even act on them), please get in touch with Franck Bardol and I as we expect these meetings to take place once a month.
Following up on 2497..2498..2499.., Andrei Bursuc became the 2,500th member of the Compresive Sensing Group on LinkedIn.
In the different conversation groups, I noticed that Kiran Varanasi mentioned his paper on the G+ group and answered a few questions from members of the group. The same question about an engineering project using compressive sensing was posted on both the Reddit group and the G+ group: pick one and answer there. Finally, Evgeniy posted a question about sparse matrices from the math stackexchange and posted it on the G+ group
The Reproducible Research movement continued on Nuit Blanche this month as new following implementations were made available
- Bayesian Robust Matrix Factorization for Image and Video Processing - implementation -
- Sparse Localized Deformation Components - implementation -
- Sparse and Functional Principal Components Analysis - implementation -
- HTOpt: Optimization on the Hierarchical Tucker manifold - applications to tensor completion -implementation -
- PYKSVD : A highly optimized, parallel implementation of the Batch-OMP version of the KSVD learning algorithm - implementation -
- Accurate Profiling of Microbial Communities from Massively Parallel Sequencing using Convex Optimization - implementation -
Slides and Videos:
- Slides: ENS/INRIA Visual Recognition and Machine Learning Summer School
- Videos and Slides: IPAM Graduate Summer School: Computer Vision
- Videos: Compressive Sensing Introduction by Mark Davenport
- Videos and Slides: IMA's course Applied Statistics and Machine Learning
- Videos: Big Data Boot Camp Day 1, Simons Institute, Berkeley
- Videos: Big Data Boot Camp Day 2,3 and 4, Simons Institute, Berkeley
Sunday Morning Insights:
- Sunday Morning Insight: A conversation on Nanopore Sequencing and Signal Processing
- Sunday Morning Insights: A follow-up on nanopore compressive sequencers and Muon tomography for locating salt domes.
Other Insights:
Around the blogs:
Saturday Morning Videos:
Meet-ups:
Jobs:
Meeting organization:
Credit: NASA/ESA
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