Past Meetup

Deep Learning Meetup #14 at Station F

This Meetup is past

130 people went

Station F

Hall Freyssinet · Paris

How to find us

Ask reception

Location image of event venue

Details

English version below

Chers Deeplearners,

Le meetup Deep Learning revient le 26/09. L'événement sera spécial cette fois, car nous aurons l'occasion de nous retrouver à Station F à 19h00, et que le Meetup est organisé en partenariat entre Heuritech et Microsoft. Il sera suivi de discussions autour de pizzas et boissons vers 21h.

Pour s'inscrire il faut impérativement s'inscrire au lien suivant en indiquant nom et prénom qui seront vérifiés à Station F (Être inscrit sur Meetup ne vous garantit en aucun cas l'accès): http://bit.ly/EventbriteHeuritech

Nous aurons l'honneur d'avoir Gül Varol et Julien Perez, chercheuse et chercheur en Deep Learning. Exceptionnellement, nous aurons également Lê Nguyên Hoang, vidéaste Youtube responsable de la chaîne Science4all. Les abstracts de leurs présentations en fin de message.

Au plaisir de vous y voir,

Charles

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Dear Deeplearners,

The Deep Learning meetup is coming back on 26/09. The event will have a special flavor, as we will meet at Station F at 19:00, and because the meetup is co-organized between Heuritech and Microsoft. Discussions, Pizzas & Drinks will follow at around 21:00.

To subscribe, you absolutely need to register using the following link with your name and surname which will be checked at Station F (Being registered here on Meetup does not guarantee any access): http://bit.ly/EventbriteHeuritech

We will be honored to have Gül Varol and Julien Perez, researchers in Deep Learning. Exceptionnally, we will also have a talk from Lê Nguyên Hoang, the French Youtuber who created the Science4all channel (Talk in French). You may find the abstracts of their presentations below.

Best,

Charles

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Gul Varol (INRIA/ENS) BodyNet: Volumetric Inference of 3D Human Body Shapes - English

Human shape estimation is an important task for video editing, animation and fashion industry. Predicting 3D human body shape from natural images, however, is highly challenging due to factors such as variation in human bodies, clothing and viewpoint. We propose BodyNet, an end-to-end trainable neural network for direct inference of volumetric body shape from a single image. First, I will present our recently released SURREAL dataset that consists of synthetic images of people whose 3D annotations are automatically collected. Then, I will show the advantages of the BodyNet components: (i) a volumetric 3D loss, (ii) a multi-view re-projection loss, and (iii) intermediate supervision of 2D pose, 2D body part segmentation, and 3D pose. Our results and the dataset open up new possibilities for advancing person analysis using cheap and large-scale synthetic data. More at: https://www.di.ens.fr/willow/research/bodynet/

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Julien Perez (Naver Labs) Machine Reading - French or English

The field of Machine Reading has recently emerged as a possible continuation of the tasks of Natural Language Processing. Given a large set of passages of text associated with questions and answers, our goal consists in learning a question answering system solely from these examples. As the research groups dedicated to Machine Reading around the globe have started to produce encouraging results, this task challenges our current understanding of deep learning and machine comprehension. In this talk, we will go through some of the current models and learning protocols associated with this task. After describing the current datasets of the domain, we will introduce ReviewQA, a dataset of relational reasoning for review understanding. Finally, we will discuss other possible applications of such an approach like dialog understanding and fact news detection.

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Lê Nguyên Hoang (Science4all Youtube Channel) Faut-il craindre une superintelligence artificielle ? - French

On va étudier les prédictions des experts et d’autres arguments autour de la possibilité d’une superintelligence artificielle, de l’éventuelle date de son émergence, et de ses conséquences.