Tuesday, June 27, 2017

Paris Machine Learning #10 Ending Season 4 : Large-Scale Video Classification, Community Detection, Code Mining, Maps, Load Monitoring and Cognitive.



This is the final episode of Season 4. Thanks to SCOR for hosting us and for the drinks and food. There will be a capacity of 130 seats (as usual : First come / first served). Most if not all presentation should be in French but the slides will be in English. Questions in either French or English are welcome.

Streaming is here:



Schedule
  • 6:30 PM doors open
  • 6:45-9:00 PM : Talks
  • 9:00-10:00 PM : socializing

Talks (and slides)



We present state-of-the-art deep learning architectures for feature aggregation. We used them in the context of video representation and explain how we won the Youtube 8M Large-Scale Video Understanding Challenge. No prior knowledge in computer vision is required to understand the work. 

Christel Beltran (IBM) Innovating, Differenciating with Cognitive
AI or Cognitive, why now, why so fast, perspectives on current use cases

La détection de communautés au sein d'un graphe permet d'identifier les groupes d'individus, ainsi que leurs dynamiques. Une introduction à cette thématique sera faite, puis deux cas d'applications seront présentés sur les réseaux sociaux : Meetup et LinkedIn. Retrouve-t-on la communauté Data Science? 

Les applications en code mining sont les mêmes qu'en text mining : génération de code, traduction automatique dans un autre langage, extraction de logique métier... pourtant, la structure d'un document de code et son contenu diffèrent fortement d'un document de texte. Dans ce talk, nous verrons quelles sont les divergences entre les langages naturels et les langages de programmation et comment ces particularités influent sur la manière de préparer puis traiter automatiquement du code source

At Qucit we use geographic data collected from hundreds of cities on a daily basis. It is collected from many different sources, often from each city’s open data website and used as inputs to our models. We can then predict parking times, bikeshare stations occupations, stress levels, parking fraud... Gathering it is a lot of fastidious work and we aim at automating it. In order to do so we want to get our data from a source available everywhere: satellite images. We now have a good enough precision to detect roads, buildings and we believe that single trees can be detected too. We tested our model on the SpaceNet images and labels, acquired thanks to the Spacenet Challenge. During this challenge the Topcoder Community had to develop automated methods for extracting building footprints from high-resolution satellite imagery. 

Non Intrusive Load Monitoring is the field of electrical consumption disaggregation within a building thus enabling people to increase their energy efficiency and reduce both energy demand and electricity costs. We will present to you this active research field and what are the learning challenges @Smart Impulse.




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Monday, June 26, 2017

Coherent inverse scattering via transmission matrices: Efficient phase retrieval algorithms and a public dataset - Dataset -

I just came upon this  work from the good folks at Rice and Northwestern that also features an attendant dataset. Enjoy !





Coherent inverse scattering via transmission matrices: Efficient phase retrieval algorithms and a public dataset by Christopher A. Metzler ; Manoj K. Sharma ; Sudarshan Nagesh ; Richard G. Baraniuk ; Oliver Cossairt ; Ashok Veeraraghavan

A transmission matrix describes the input-output relationship of a complex wavefront as it passes through/reflects off a multiple-scattering medium, such as frosted glass or a painted wall. Knowing a medium's transmission matrix enables one to image through the medium, send signals through the medium, or even use the medium as a lens. The double phase retrieval method is a recently proposed technique to learn a medium's transmission matrix that avoids difficult-to-capture interferometric measurements. Unfortunately, to perform high resolution imaging, existing double phase retrieval methods require (1) a large number of measurements and (2) an unreasonable amount of computation.

In this work we focus on the latter of these two problems and reduce computation times with two distinct methods: First, we develop a new phase retrieval algorithm that is significantly faster than existing methods, especially when used with an amplitude-only spatial light modulator (SLM). Second, we calibrate the system using a phase-only SLM, rather than an amplitude-only SLM which was used in previous double phase retrieval experiments. This seemingly trivial change enables us to use a far faster class of phase retrieval algorithms. As a result of these advances, we achieve a $100\times$ reduction in computation times, thereby allowing us to image through scattering media at state-of-the-art resolutions. In addition to these advances, we also release the first publicly available transmission matrix dataset. This contribution will enable phase retrieval researchers to apply their algorithms to real data. Of particular interest to this community, our measurement vectors are naturally i.i.d.~subgaussian, i.e., no coded diffraction pattern is required.
Dataset:

A public dataset containing the transmission matrices of various scattering media can be downloaded here.

The properties of the TM are briefly described here. 



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