Tuesday, January 31, 2017

Nuit Blanche in Review (January 2017)


A few things happened since the last Nuit Blanche in Review. We had a few implementations, a thesis, few jobs announcements and the NIPS videos....Enjoy !

Also, Laurent is current at Photonics West this week !

Implementation

Thesis

Sunday Morning Insight
Other in-depth insights
Book
Job:
Paris Machine Learning meetup

Videos:



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Monday, January 30, 2017

Wasserstein GAN / Towards Principled Methods for Training Generative Adversarial Networks

We mentioned GANs before. Here are contributions on how to use Earth Mover's distances to improve their training (Cedric Villani is mentioned in the references of the second paper and points to a newer version of the Optimal transport, old and new manuscript)


Wasserstein GAN by Martin Arjovsky, Soumith Chintala, Léon Bottou

We introduce a new algorithm named WGAN, an alternative to traditional GAN training. In this new model, we show that we can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches. Furthermore, we show that the corresponding optimization problem is sound, and provide extensive theoretical work highlighting the deep connections to other distances between distributions.

Towards Principled Methods for Training Generative Adversarial Networks by Martin Arjovsky, Léon Bottou

The goal of this paper is not to introduce a single algorithm or method, but to make theoretical steps towards fully understanding the training dynamics of generative adversarial networks. In order to substantiate our theoretical analysis, we perform targeted experiments to verify our assumptions, illustrate our claims, and quantify the phenomena. This paper is divided into three sections. The first section introduces the problem at hand. The second section is dedicated to studying and proving rigorously the problems including instability and saturation that arize when training generative adversarial networks. The third section examines a practical and theoretically grounded direction towards solving these problems, while introducing new tools to study them.
 



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Saturday, January 28, 2017

Saturday Morning Videos: Fairness, Accountability, and Transparency in Machine Learning @NYU School of Law

 The program is here.

 
Fairness, Accountability, and Transparency in Machine Learning: Part One
 
Fairness, Accountability, and Transparency in Machine Learning: Part Two
 
Fairness, Accountability, and Transparency in Machine Learning: Part Three

Fairness, Accountability, and Transparency: Part Four



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Saturday Morning Videos: Foundations of Machine Learning Boot Camp, Simons Institute, Berkeley


This past week saw a series of tutorial presentations in a "bootcamp" on the Foundations of Machine Learning at the Simons Institute at Berkeley organized by Sanjoy Dasgupta, Sanjeev Arora , Nina Balcan, Peter Bartlett, Sham Kakade, Santosh Vempala. Links to the videos can be found directly from the page:

The Boot Camp is intended to acquaint program participants with the key themes of the program. It will consist of five days of tutorial presentations, each with ample time for questions and discussion, as follows:
Monday, January 23rd
  
Tuesday, January 24th
Wednesday, January 25th
Thursday, January 26th
Friday, January 27th


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Saturday Morning Videos: Deep Learning Symposium, #NIPS2016

Here are the three sessions of the Deep Learning Symposium  at NIPS2016, enjoy !
01:54:39

Friday, January 27, 2017

Job: Postdoc, Research Associate in Data Science, UCL, London

Jason just sent me the following:

Hi Igor,

I just wanted to let you know about a related vacancy for a postdoctoral researcher at UCL.
Would you consider posting on Nuit Blanche if you think it suitable?
Many thanks in advance for your help ....
Best wishes,
Jason 

Here is the announcement:
Research Associate in Data Science


We are seeking an excellent postdoctoral researcher in signal processing, applied mathematics, physics, statistics, computer science, or a related field, to develop novel signal analysis techniques for extracting scientific information from large observational data-sets. Research aims will include a focus on signal analysis on the sphere but can be extended to incorporate the interests and expertise of the successful applicant.

Signals defined on the sphere are prevalent in a diverse range of fields, including cosmology, geophysics, acoustics, and computer graphics, for example. In cosmology, observations made by the European Space Agency's Planck and Euclid satellites live on the celestial sphere, leading to very large and precise spherical data-sets, the robust analysis of which can reveal a great deal about the nature of our Universe. The successful applicant will focus on the theoretical and methodological foundations of the analysis of signals defined on the sphere and will work closely with another postdoctoral researcher who will fill a complementary post focused on application to observational data.

The successful applicant will be based in the Astrophysics Group at the Mullard Space Science Laboratory (MSSL) of University College London (UCL) and will join the multi-disciplinary astroinformatics team, which comprises researchers with expertise in astrophysics, applied mathematics, signal processing, statistics and software engineering. The goal of the astroinformatics team is to develop and apply novel analysis techniques to extract scientific information from very large data sets, in order to develop a deeper understanding of the fundamental physics underlying the evolution of our Universe. The successful applicant will work closely with Dr Jason McEwen, while also interacting with other members of the astroinformatics team and the Astrophysics Group.

Further information is available at http://www.jasonmcewen.org/opportunities.html.
--
www.jasonmcewen.org


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Controlling Light Transmission Through Highly Scattering Media Using Semi-Definite Programming as a Phase Retrieval Computation Method




Complex Semi-Definite Programming (SDP) is introduced as a novel approach to phase retrieval enabled control of monochromatic light transmission through highly scattering media. In a simple optical setup, a spatial light modulator is used to generate a random sequence of phase-modulated wavefronts, and the resulting intensity speckle patterns in the transmitted light are acquired on a camera. The SDP algorithm allows computation of the complex transmission matrix of the system from this sequence of intensity-only measurements, without need for a reference beam. Once the transmission matrix is determined, optimal wavefronts are computed that focus the incident beam to any position or sequence of positions on the far side of the scattering medium, without the need for any subsequent measurements or wavefront shaping iterations. The number of measurements required and the degree of enhancement of the intensity at focus is determined by the number of pixels controlled by the spatial light modulator.





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Wednesday, January 25, 2017

The #NIPS2016 Videos are out

The videos for the Neural Information Processing Systems Conference - NIPS 2016 are out (first three days), enjoy !















SDP Relaxation with Randomized Rounding for Energy Disaggregation












































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