Nuit Blanche: 01/01/2017

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distances to improve their training (Cedric Villani is mentioned in the references of the second paper and points to a newer version of the Optimal transport, old and new manuscript)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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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.
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www.jasonmcewen.org