Nuit Blanche in Review (November 2016)

Since the last Nuit Blanche in Review (October 2016), a somewhat very interesting idea came out in the form of this paper

• Seeing it all: Convolutional network layers map the function of the human visual system

Identifying how certain parts of the brain do a specific computation is indeed an awesome idea !

We had a one implementation, a few in-depth papers but we also saw the release of numerous papers for NIPS and about 500 submissions for a 2 year old conference (ICLR)! Among these papers, one has drawn the attention:

• Direct Feedback Alignment Provides Learning in Deep Neural Networks

It seems to promise a faster training time in Deep Learning. We'll see. In the meantime, The Paris Machine Learning meetup had to meetup (one regular and one 'Hors série') but most importantly, we have a website:

http://MLParis.org

Enjoy the rest of the review !

Implementation

• Manopt 3.0 released - implementation 

In-depth

• Direct Feedback Alignment Provides Learning in Deep Neural Networks
• Efficient Convolutional Auto-Encoding via Random Convexification and Frequency-Domain Minimization
• Faster Kernel Ridge Regression Using Sketching and Preconditioning / Sharper Bounds for Regression and Low-Rank Approximation with Regularization
• Learning Kernels with Random Features
• An Elementary Proof of Convex Phase Retrieval in the Natural Parameter Space via the Linear Program PhaseMax / Compressed Sensing from Phaseless Gaussian Measurements via Linear Programming in the Natural Parameter Space
• Single-view phase retrieval of an extended sample by exploiting edge detection and sparsity
• Rapid, Robust, and Reliable Blind Deconvolution via Nonconvex Optimization / Self-Calibration via Linear Least Squares
• Stochastic CoSaMP: Randomizing Greedy Pursuit for Sparse Signal Recovery
• Lets keep it simple: using simple architectures to outperform deeper architectures
• Ternary Weight Decomposition and Binary Activation Encoding for Fast and Compact Neural Network / Sparsely Connected Neural Networks: Towards Efficient VLSI Implementation of Deep Neural Networks
• Understanding Neural Sparse Coding with Matrix Factorization

Conferences

• CfP: SPARS 2017, Signal Processing with Adaptive Sparse Structured Representations
• NIPS: Advances In Neural Information Processing Systems 29 (NIPS 2016) pre-proceedings
• ICLR 2017: Sparse Coding, Canonical Correlation Analysis and Dictionary Learning
• ICLR2017: Identity and Invariance
• ICLR 2017: Lighter Networks
• Understanding Neural Sparse Coding with Matrix Factorization

Theses

• Thesis: Robust Large Margin Approaches for Machine Learning in Adversarial Settings

Paris Machine Learning

• Paris Machine Learning Hors Serie #4 Season 4 on Deeplearning4j: Applying Deep Learning to business problems in production
• Paris Machine Learning Meetup #3 Season 4: OPECST, Correlations, Transfer Learning, DL @Amazon, Car Sales
• An Interesting Policy Event Tomorrow at the Paris Machine Learning meetup

Job

• CSjob: 2 year Postdoc, echOpen, Paris, France
• CSjob: Postdoc on missing values, Data sciences team, CMAP - Ecole Polytechnique , Paris, France

Videos:

• Saturday Morning Videos: Machine Learning Conference SF, November 11th, 2016
• Videostream: 3rd Workshop on Fairness, Accountability, and Transparency in Machine Learning
• Saturday Morning Video: Random Projections for Probabilistic Inference, Stefano Ermon
• Saturday Morning Video: Error error error correcting correcting correcting codes codes codes, Mary Wootters
• Saturday Morning Videos: Hugo Larochelle's neural network & deep learning tutorial videos, subtitled & screengrabbed

Sunday Morning Insight

• Sunday Morning Insight: Antipodal Seismic Maps ?
• Sunday Morning Insight: The Clear and Present Dangers

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