Nuit Blanche in Review ( December 2014 )

Since the last Nuit Blanche in Review ( November 2014 )
we had much machine learning related entries as we are seeing a decreasing gap between what we currently see as reconstruction solvers in compressive sensing and the current deep (or not) architectures used in Machine learning to perform classification. Without further ado :

We had two implementations this month:

• Learning with Fredholm Kernels - implementation
• Learning Multidimensional Fourier Series With Tensor Trains - implementation - 

 Three "insights"

• Sunday Morning Insight: The Stuff of Discovery
• Sunday Morning Insight: The Regularization Architecture
• Sunday Morning Insight: An exact mapping between the Variational Renormalization Group and Deep Learning

a few books and theses:

• Book: Sketching as a Tool for Numerical Linear Algebra
• Thesis : Convex Optimization Algorithms and Recovery Theories for Sparse Models in Machine Learning, Bo Huang
• Thesis: Quantum Algorithms for Linear Algebra and Machine Learning; Anupam Prakash
• Book: The Discrepancy Method by Bernard Chazelle

Some more in-depth coverage:

Compressive Sensing

• Single-shot compressed ultrafast photography at one hundred billion frames per second
• How little data is enough? Phase-diagram analysis of sparsity-regularized X-ray CT
• Compressive Hyperspectral Imaging with Side Information
• More Oil and Compressive Sensing Connections
• Another Donoho-Tao Moment ?
• Hamming's time: Scientific Discovery Enabled by Compressive Sensing and related fields Does Compressed Sensing have applications in Robust Statistics?
• Detecting defects in solar cells using compressive sensing 

 Machine Learning

• Playing with Duality: An Overview of Recent Primal-Dual Approaches for Solving Large-Scale Optimization Problems
• Streaming Anomaly Detection Using Online Matrix Sketching
• Unsupervised Learning of Spatiotemporally Coherent Metrics
• Tag-Aware Ordinal Sparse Factor Analysis for Learning and Content Analytics
• A la Carte - Learning Fast Kernels
• On the Stability of Deep Networks
• NIPS2014 Poster papers   
• Deep Fried Convnets
• Non-parametric PSF estimation from celestial transit solar images using blind deconvolution  
• Distinguishing Cause from Effect using Observational Data: Methods and Benchmarks
• Cauchy Principal Component Analysis  
• Cone-constrained Principal Component Analysis
  Sparse Polynomial Learning and Graph Sketching
  A statistical model for tensor PCA 
• L_1 regularization in Machine Learning: Memory Bounded Deep Convolutional Networks / Sparse Random Features Algorithm as Coordinate Descent in Hilbert Space

Paris Machine Learning Meetup

• Paris Machine Learning Meetup #4 Season 2: Tips and advices for machine learning challenges, Biochemical Probabilistic Computation, Bias-variance decomposition in Random Forests 

 Video and Slides / Slides

• Video : Statistical and Causal Approaches to Machine Learning, Bernhard Schölkopf.
• Video: Exact Recovery via Convex Relaxations 
• Saturday Morning Videos : Spectral Algorithms: From Theory to Practice (Simons Institute @ Berkeley)
• Saturday Morning Videos : Semidefinite Optimization, Approximation and Applications (Simons Institute @ Berkeley)
• Video and Slides: Analysis and Design of Optimization Algorithms via Integral Quadratic Constraints Video: A Statistical Model for Tensor Principal Component Analysis
• Video and Slides: Random Embeddings, Matrix-valued Kernels and Deep Learning 
• Slides: Curse of Dimensionality with Convex Neural Networks by Francis Bach 

Jobs:

• CSjobs: Marie Curie Early Stage Researchers in Sparse Representations and Compressed Sensing
• Job: Research positions (PostDocs and PhD studentship) at MSSL UCL
• CSjob: Postdoc in Mathematics
• Job: One year postdoc in the area of Applied Nonlinear Fourier Analysis, TU Delft
• CSjobs: Multiple positions @ BASP Edinburgh  

Other videos:

• Saturday Morning Video: Jeff Iliff: One more reason to get a good night’s sleep
• Video: Alexander Gerst’s Earth timelapses

A few new statistics

• Nuit Blanche in Numbers
• Three and a half million page views: a million here, a million there and soon enough we're talking real readership...

 Credit: Courtesy of NASA/SDO and the AIA, EVE, and HMI science teams.

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