Nuit Blanche in Review (June 2016)

Since the last Nuit Blanche in Review (May 2016), Juno inserted into Jupiter's orbit, We started LightOn,  Nuit Blanche reached five million page views. We also had our last Paris Machine Learning meetup of season 3 and heard about how Machine Learning had begun operations on Mars and much much more. In fact, it's all listed here. Enjoy !

Implementations

• DropNeuron: Simplifying the Structure of Deep Neural Networks - implementation -
• FMR: Fast randomized algorithms for covariance matrix computations - implementation -
• Highly Technical Reference Page: Laplacian Linear Equations, Graph Sparsification, Local Clustering, Low-Stretch Trees, etc. + implementation
• RedEye: Analog ConvNet Image Sensor Architecture for Continuous Mobile Vision - implementation -
• Discovering Causal Signals in Images - implementation -
• RSVDPACK: Subroutines for computing partial singular value, interpolative, and CUR decompositions via randomized sampling on multi core and GPU architectures - implementation -
• A Deep Learning Approach to Block-based Compressed Sensing of Images - implementation -
• A Riemannian gossip approach to decentralized matrix completion - implementation -

Theses

• Thesis: Statistical physics of linear and bilinear inference problems by Christophe Schülke
• Thesis: Rich and Efficient Visual Data Representation, Mohammad Rastegari
• Thesis: Recovering structured signals in high dimensions via non-smooth convex optimization: Precise performance analysis by Christos Thrampoulidis
• Thesis: Learning with Scalability and Compactness by Wenlin Chen
• Thesis: From dependence to causation by David Lopez-Paz
• Thesis: Cosparse regularization of physics-driven inverse problems, Srđan Kitić

Around the blogs

• Around The Blogs in 78 Summer hours

Surveys/Reviews

• Literature survey on low rank approximation of matrices
• Optimization Methods for Large-Scale Machine Learning
• Kernel Mean Embedding of Distributions: A Review and Beyonds
• A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention

In-depth

• Group Sparse Regularization for Deep Neural Networks
• The "Horse'' Inside: Seeking Causes Behind the Behaviours of Music Content Analysis Systems
• Learning to learn by gradient descent by gradient descent
• Time for dithering: fast and quantized random embeddings via the restricted isometry property
• A Powerful Generative Model Using Random Weights for the Deep Image Representation
• Large-Scale Kernel Methods for Independence Testing
• Sketching for Large-Scale Learning of Mixture Models
• Learning Infinite-Layer Networks: Beyond the Kernel Trick
• Recycling Randomness with Structure for Sublinear time Kernel Expansions
• Image Restoration and Reconstruction using Variable Splitting and Class-adapted Image Priors / Image Restoration with Locally Selected Class-Adapted Models
• Of Li-Ion Batteries and Biochemical networks: Finding a Low Dimensional Models in Haystacks
• Compressive light-field microscopy for 3D neural activity recording
• SparkleGeometry: Glitter Imaging for 3D Point Tracking / Cloudmaps from Static Ground-View Video

CS/ML Hardware:

• RedEye: Analog ConvNet Image Sensor Architecture for Continuous Mobile Vision - implementation -

Meetings/Meetups/conferences:

• Ce Soir: Paris Machine Learning Meetup #14 Season 3: Dare Mighty Things
• international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST)
• Research workshop: HORSE 2016 On “Horses” and “Potemkin Villages” in Applied Machine Learning at QMUL, London
• Slides, papers some videos: ICML, CVPR, NIPS
• CVPR papers are out !

Jobs:

• CSjob: Two Postdocs, C-SENSE: Exploiting low dimensional signal models for sensing, computation and processing, Edinburgh,, Scotland.
• CSjob: Postdoc, Signal processing & inverse problems for the future SKA, Nice, France
• Post-doc Causality and Machine Learning, University Paris-Saclay, France

Videos:

• Saturday Morning Video: Machine Learning in Computational Biology Workshop, @NIPS2015
• Saturday Morning Videos: Nonparametric Methods for Large Scale Representation Learning NIPS 2015 Workshop
• Saturday Morning Videos: Probabilistic Integration Workshop at NIPS 2015 (and slides)
• Saturday Monring Videos: Deep Reinforcement Learning Workshop @NIPS 2015 (and slides)
• Saturday Morning Videos: Machine Learning For Healthcare (MLHC) workshop @NIPS2015

LightOn

• First Light
• LightOn. And so it begins. 

Nuit Blanche

• Five million page views: The Numbers Game in Long Distance Blogging

 

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