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.

Video of the meetup is here:

  • 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.

Join the CompressiveSensing subreddit or the Google+ Community or the Facebook page and post there !

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