This past month, we remembered the Past (Remembering Columbia STS-107, The Graceful Rendez-Vous Pitch Maneuvers) and saw the Future (Predicting the Future: The Upcoming Stephanie Events). We learned that Compressive Sensing Skews Everything including Terry Tao's h-index. We wondered if Bad Learning was Learning ? Submitted From Direct Imaging to Machine Learning … and more, in 15 minutes to JIONC whose organizers decided to put on a poster (how do you convert 15 minutes on a poster?). I also provided some tips on Organizing Meetups and got Nuit Blanche featured on La Recherche of February. But this is nothing compared to the slow recognition that Machine Learning techniques like RKHS may be a way to more deeply connected nonlinear sening and compressive sensing.

In the meantime, a large number of implementations were made available by their respective authors:

- MISO: Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning
- Avoiding pathologies in very deep networks
- Interest Zone Matrix Approximation
- REMODE: Probabilistic, Monocular Dense Reconstruction in Real Time
- Randomized LU Decomposition
- Nonlinear estimation of material abundances in hyperspectral images with L1-norm spatial regularization
- Kernel LMS algorithm with forward-backward splitting for dictionary learning
- Blind Image Deblurring with Unknown Boundaries Using the Alternating Method of Multipliers
- Sparse Coding and Dictionary Learning for Symmetric Positive Definite Matrices: A Kernel Approach
- SparseFHT: A Fast Hadamard Transform for Signals with Sub-linear Sparsity in the Transform Domain - implementation -

We had two Sunday Morning Insights

Specific Focused entries

- Extrinsic Methods for Coding and Dictionary Learning on Grassmann Manifolds
- Signal to Noise Ratio in Lensless Compressive Imaging / Compressive and Adaptive Millimeter-wave SAR / Efficient Low Dose X-ray CT Reconstruction
- Video Compressive Sensing for Dynamic MRI
- Compressed sensing fMRI using gradient-recalled echo and EPI sequences
- Sparse Estimation From Noisy Observations of an Overdetermined Linear System / On the Randomized Kaczmarz Algorithm
- Phase Retrieval for Sparse Signals: Uniqueness Conditions
- Improved Recovery Guarantees for Phase Retrieval from Coded Diffraction Patterns / Sparse phase retrieval via group-sparse optimization
- Small ball probabilities for linear images of high dimensional distributions / Circular law for random matrices with exchangeable entries
- Model-based Sketching and Recovery with Expanders / For-all Sparse Recovery in Near-Optimal Time
- Sparse Factor Analysis for Learning and Content Analytics
- Properties of spatial coupling in compressed sensing / On Convergence of Approximate Message Passing

But also

CAI:

Meetups:

**Join the CompressiveSensing subreddit or the Google+ Community and post there !**

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