Nuit Blanche in Review (December 2016).

Happy New Year 2017 ! Since the Nuit Blanche in Review (November 2016), NIPS2016 happened and a few other important things ! As a reminder, we are still in this period of intense intellectual wandering, What Are You Waiting For ?

This past month, we also had a few implementations:

• Learning the nonlinear geometry of high-dimensional data: Models and algorithms - implementation 
• DataSketches : Sketches Library from Yahoo! - implementation -
• SketchMLbox: A MATLAB toolbox for large-scale mixture learning - implementation - ( Compressive K-means , Sketching for Large-Scale Learning of Mixture Models )
• Linearly Convergent Randomized Iterative Methods for Computing the Pseudoinverse -implementation -
• Thesis: Sketch and Project: Randomized Iterative Methods for Linear Systems and Inverting Matrices - implementation -
• Onsager-Corrected Deep Networks for Sparse Linear Inverse Problems - tensorflow implementation-

Some in-depth blog entries:

• Sparse Label Propagation
• Efficient Methods for Deep Neural Networks
• Interpretable Recurrent Neural Networks Using Sequential Sparse Recovery
• Distributed Sequence Memory of Multidimensional Inputs in Recurrent Networks

Two theses

• Thesis: Sketch and Project: Randomized Iterative Methods for Linear Systems and Inverting Matrices - implementation -
• Thesis: Uncertainty in Deep Learning by Yarin Gal

A few entries covering NIPS2016

• #NIPS2016: Interpretable Machine Learning for Complex Systems Workshop
• 22 implementations of #NIPS2016 papers
• Efficient Methods for Deep Neural Networks
• Interpretable Recurrent Neural Networks Using Sequential Sparse Recovery
• Distributed Sequence Memory of Multidimensional Inputs in Recurrent Networks
• Some general takeaways from #NIPS2016
• Spotlight videos at #NIPS2016
• Videos at #NIPS2016, and other news and announcements
• What to do in Barcelona this week, #NIPS2016 edition

Paris Machine Learning meetup

• Tonight: Paris Machine Learning #4 Season 4: ML for Ultrasound, export/import in R, electric consumption from times series, #NIPS2016

Slides:

• Slides: Annual Paris-Saclay Center for Data Science Pitching Day
• Slides: New Directions for Learning with Kernels and Gaussian Processes, Dagstuhl Seminar

Jobs:

• Internship: Signal and image classification with invariant descriptors (scattering transforms), IFPEN Rueil Malmaison, France
• Job: Data scientist: deep learning approaches to microbial genomics, Institut Pasteur, Paris
• Job: Data Scientist Position, Paris-Saclay Center for Data Science (CDS), France
• Job: Postdoc Position in Machine Learning for Health Sensing, MIT
• CSjob: PhD. position, Compressed sensing in transmission electron microscopy, EMAT, Antwerp, Belgium

Saturday Morning Videos:

• Saturday Morning Videos: Learning, Algorithm Design and Beyond Worst-Case Analysis workshop, Simons Institute, Berkeley
• Saturday Morning Video: Deep Compression, DSD Training and EIE: Deep Neural Network Model Compression, Regularization and Hardware Acceleration by Song Han

Conferences:

• CfP: SPARS 2017, Lisbon, Portugal, June 5-8, 2017, Submission deadline (EXTENDED): January 3, 2017 **

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