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:

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"

a few books and theses:

Some more in-depth coverage:

Compressive Sensing

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 - 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

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

- 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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