Nuit Blanche in Review (October and November 2017)

It's been two months since the last Nuit Blanche in Review (September 2017). We've had two Paris Machine Learning meetups, a two-day meeting of France is AI. Nuit Blanche featured two theses, a few job postings. While NIPS 2017 is about to start. I also recall last year's NIPS in Barcelona, where there was a sense that the community would move in on other areas besides computer vision. From some general takeaways from #NIPS2016

• With the astounding success of Deep Learning algorithms, other communities of science have essentially yielded to these tools in a manner of two or three years. I felt that the main question at the meeting was: which field would be next ? Since the Machine Learning/Deep Learning community was able to elevate itself thanks to high quality datasets such as MNIST all the way to Imagenet, it is only fair to see where this is going with the release of a few datasets during the conference including the Universe from OpenAI. Control systems and simulators (forward problems in science) seem the next target.

Well, if you take a look at the few papers of this past two months mentioned here on Nuit Blanche, it looks like GANs and other methods have essentially made their way into the building of recovery solvers: i.e. algorithms dedicated to build images/data back from measurements. The recent interest in the development of Deep Learning for physics  makes it likely we will soon build better sensing hardware. 

Another interesting item to us at LightOn this past month is the realization that Biologically Inspired Random Projections is a thing. 

Enjoy the postings.

Implementation

• One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection Models - implementation -
• Learned D-AMP: Principled Neural Network based Compressive Image Recovery - implementation -

In-depth

• Globally and Locally Consistent Image Completion
• Deep Generative Adversarial Networks for Compressed Sensing Automates MRI - implementation - / Recurrent Generative Adversarial Networks for Proximal Learning and Automated Compressive Image Recovery
• Optimizing Kernel Machines using Deep Learning / Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations
• ForestHash: Semantic Hashing With Shallow Random Forests and Tiny Convolutional Networks
• Phase Transitions, Optimal Errors and Optimality of Message-Passing in Generalized Linear Models
• Compressed Sensing MRI Reconstruction with Cyclic Loss in Generative Adversarial Networks
• DCASE 2017 TASK 1: Acoustic Scene Classification Using Shift-Invariant Kernels and Random Features
• Linear Dimensionality Reduction in Linear Time: Johnson-Lindenstrauss-type Guarantees for Random Subspace / Structure-aware error bounds for linear classification with the zero-one loss
• Compressed Sensing MRI Reconstruction with Cyclic Loss in Generative Adversarial Networks

Hardware

• DiffuserCam: Lensless Single-exposure 3D Imaging

Thesis

• Thesis: Sketching for Large-Scale Learning of Mixture Models by Nicolas Keriven
• Thesis: Efficient Methods and Hardware for Deep Learning by Song Han

Meetup

• France is AI, Program for Day One
• Ce soir: Paris Machine Learning #3 Season 5, PokémonGO, Unsupervised ML in high dimension, Prevision.io, Learning to program
• Tonight: Paris Machine Learning Meetup #2 Season 5: Reinforcement Learning for Malware detection, Fraud detection, video scene detection and Climate Change

Videos and slides:

• Slides and Videos: Deep RL Bootcamp 26-27 August 2017, Berkeley CA
• Slides: Université Cote d'Azur/UCA Deep Learning School, May 2017

CfP

• CfP: Workshop on Approximating high dimensional functions, Alan Turing Institute, London,18-19 December 2017.

Job:

• CSJob: 2 Postdocs, Computer Vision and Machine Learning for Robotics / Navigation, decision making and teleoperation of autonomous vehicles, INRIA, France
• Job: Faculty position, ECE, Ohio State
• CSJob: Four Postdoctoral Research Assistants, Mathematical Institute and the Oxford-Emirates Data Science Lab (OEDSL)
• Job: Harvard University Data Science Initiative Postdoctoral Fellows Program
• Job: Post-doctoral fellow/PhD position in statistical genomics and imaging analysis, Tulane University, LA
• Jobs: Two Postdocs, MIT Institute for Foundations of Data Science (MIFODS)

credit: NASA / JPL / Ricardo Nunes

TVASHTAR PATERAE on Io FROM GALILEO ORBIT I27

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