Around The Blogs in 78 Hours

We got pre-selected into the iLab competition, woohoo !

Ben Lorica just wrote about tensors in Let’s build open source tensor libraries for data science . One of the author whose work was featured in that blog, Anima Anandkumar, kindly responded to one of my comment there:

 ...Indeed, there are many types of decompositions on tensors, we are certainly not the only ones to be working on them. The paper you point out about tensors states that there can be ill-posed tensors, which is true. In fact, there is another paper which states that most tensor problems are NP-hard http://arxiv.org/abs/0911.1393 However, the tensors we study, which are relevant for machine learning, turn out to be "easy cases" establish is that under some very reasonable non-degeneracy. I talk about some of these points in the podcast that Ben will post in due course. Stay tuned!

I look forward to the podcast 

Within the blogs we generally cover, here are some items that grabbed my interest:

 Sebastien

• Deep stuff about deep learning?

Djalil

• Entropy ubiquity

John

• A subtle way to over-fit

Charles

• Convex Relaxations of Transductive Learning
• Why does Deep Learning work?

Afonso

• Optimizing in smooth waters: optimization on manifolds

Dirk

• Farewell Ernie Esser

Ben

• How Digital Light Processing (DLP) works

While on Nuit Blanche, we had:

• Infinite-dimensional $\ell^1$ minimization and function approximation from pointwise data
• Devising Measurement Matrices
• On aurait pu regarder l'éclipse à Paris ....
• Single and multiple snapshot compressive beamforming
• Thesis: Spectral classification using convolutional neural networks, Pavel Hála - implementation -
• Everybody wants to be shallow: Compact Nonlinear Maps and Circulant Extensions
• Meeting: missDATA 2015, Rennes, Brittany, France
• Enhanced Image Classification With a Fast-Learning Shallow Convolutional Neural Network
• Adaptive-Rate Sparse Signal Reconstruction With Application in Compressive Background Subtraction
• DECOPT - DEcomposition Convex OPTimization - implementation -
• Variational Inference for Sparse Spectrum Approximation in Gaussian Process Regression -implementation -
• Slides: Stanford's CS231n: Convolutional Neural Networks for Visual Recognition
• Saturday Morning Videos: Nando de Freitas' Machine Learning course (Lecture 7 to 12) and slides
• Various Announcements
• High-resolution and broadband all-fiber spectrometers
• Learning Co-Sparse Analysis Operators with Separable Structures
• No Pressure
• VLSI Design of a Monolithic Compressive-Sensing Wideband Analog-to-Information Converter (and other FPGA implementations)
• Ce soir / Tonight : Paris Machine Learning Meetup #7 Season 2: Automatic Statistician, ML et Entreprise, Algo Fairness/ Certifying and removing Disparate Impact
• Ernie Esser's Passing and Legacy
• Slides: Learning Sparse Representations for Signal Processing @IISc
• A new Highly technical Reference Page: The Superiorization Methodology and Perturbation Resilience of Algorithms
• Sunday Morning Insight: Another Strange PSF in the Wild
• Saturday Morning Videos: February Fourier Talks 2015 Proceedings
• Video (and attendant code): A 3D Map of the Human Genome
• Around the blogs: The great Convergence in Action
• Video: Rambus Lensless Smart Sensor Demo at MWC 2015
• Phase Transitions in Sparse PCA
• Sparse Coding: 20 years later
• Thesis: Gravimetric Anomaly Detection using Compressed Sensing by Ryan Kappedal
• Nuit Blanche in Review ( February 2015 )

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