Wednesday, September 13, 2017

Paris Machine Learning #1 Season 5: Code Mining, Mangas, Drug Discovery, Open Law, RAMP

So Season 5 of the Paris Machine Learning meetup starts today, woohoo ! The video of the streaming for the meetup can be found below. 


Thanks to Deep Algo for hosting this meetup and sponsoring the food and drinks afterwards. 

LightOn is sponsoring the streaming.


6:30 PM doors opening ; 6:45 PM : talks beginning ; 9:00 PM : talks ending
10:00 PM : end


Short: Franck Bardol, Igor Carron, We know what the AI world did Last Summer 
Short: Xavier Lagarrigue, Presentation de Deep Algo, La Piscine
Dans le cadre des Journées nationales de l'ingénieur, L'IESF organise avec le CDS un challenge en vision par ordinateur consistant à classifier les différentes especes de pollinisateurs. Un prix sera remis à la JNI le 19 Octobre à l'UNESCO. Le peu de nombre d'exemple dans la majorité des classes rend le challenge techniquement intéressant (one shot learning / domain adaptation). lien :
Short: Open Law: IA et droit, Dataset d'apprentissage , Olivier
Abstract : L’association Open Law Le droit ouvert*, avec le soutien de la CNIL et de la Cour de Cassation, a décidé de créer un jeu de données d’apprentissage dans le domaine juridique. L'objectif est de zoner les décisions de justice des cours d'appel (discourse parsing). L’annotation est en cours et le jeu de données sera rendu public début décembre..
15 minutes presentations:

Challenges in code mining, Information theoretic approach, Jérôme Forêt, Head of R&D de Deep Algo, Deep Algo - English-

The mission of Deep Algo is to make code understandable by anyone. This involves automatic extraction of the business logic from a code base. One of the challenges is to understand the developper's intentions that led to a specific organization of this business logic.
Using Posters to Recommend Anime and Mangas by Jill-Jênn Vie,  (livestream from Japan) - English-
The classic recommendation problem is the following: given a user and the items (mangas) that they like, how can we recommend new items (mangas) that they are also likely to enjoy? Typically this is done via collaborative filtering, i.e. people with similar taste also enjoy other mangas, so we recommend these to the original user. A very common problem occurs when you have a new or obscure manga, aka the cold-start problem. There are no reviews to use for this manga, so a cooler option is to build a system that actually understands the content it recommends. We propose extracting visual information from the posters of these little-known mangas, using a deep neural net called Illustration2Vec. The theory is that users that like mangas with "girl with sword" will also like other mangas that have "girl with sword" or perhaps "girl with bow" but probably not "multiple boys in a swimming pool".
Site: http://research.mangaki.frRelevant ArXiv: Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario,

Early-stage drug discovery requires a constant supply of new molecules, to be fed into High Throughput Screening robots. To increase this supply, virtual molecules can be generated on-demand with neural networks. In this talk, I present a Reinforcement Learning generative model, and a variant using Generative Adversarial Networks. I also present two challenges that both are facing: 1. multitasking
between different objectives and 2. generating chemically diverse molecules. Finally, I sketch how these generative models could become a useful proof-of-work for a 'Drugcoin' crypto-currency, in place of the 'useless' Hashcash proof-of-work of Bitcoin. 

Motivated by the shortcomings of traditional data challenges, we have developed a unique concept and platform, called Rapid Analytics and Model Prototyping (RAMP), based on modularization and code submission.
Open code submission allows participants to build on each other’s ideas, provides the organizers with a fully functioning prototype, and makes it possible to build complex machine learning workflows while keeping the contributions simple. Besides running public data challenges, the tool may also be useful for managing the building of data science workflows internally in a data science team. In the presentation I will focus on what you can use the tool for if you are a data scientist, a student, or a data science instructor. Links:

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

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