Lisa_seminaires Décembre 2017

lisa_seminaires@iro.umontreal.ca

3 participants
5 discussions

Thesis proposal by Tristan Sylvain, Monday 9:30 @ 3195
by Yoshua Bengio 18 Déc '17

18 Déc '17

Hello, Tristan Sylvain will present his thesis proposal Monday 9:30-11:30 at PAA 3195. Everyone is welcome. Jury: President: Simon Lacoste-Julien Supervisor: Yoshua Bengio Member: Ioannis Mitliagkas Deep Neural Networks applied to medical data and Zero-Shot learning This presentation details two ongoing projects, applying deep learning to the problem of survival analysis for medical data, and using an adversarial approach to zero-shot learning. These very different problems have in common the fact that deep learning allows the learning of efficient representations of data that permit generalization when dealing with both highly noisy (in the medical case) and weakly supervised data (the zero-shot learning case). For the survival data, we used a multi-objective training procedure that allowed us to attain state of the art on many different public datasets, when compared to classical methods such as the Cox model. This presentation will provide both insights on the architecture used, and the medical context. Overall this problem is an interesting instance of applying deep learning to medical problems. Zero-shot learning is an active field of machine learning characterized by the fact that the classes we perform test time classification on are all unseen during the training procedure. To compensate for the lack of information on these classes, we are usually given a set of semantic attributes to allow some form of transfer from a model learned on seen train classes. This part of the presentation will present a current project aiming to use conditional generative adversarial networks to generate fake prototypes for unseen classes in a zero-shot learning context. I will present current results, and some of the challenges that lie ahead.

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Benjamin Scellier, PREDOC, 11 décembre à 10h00
by Aaron Courville 09 Déc '17

09 Déc '17

Bonjour, Vous êtes tous cordialement invités à assister à la présentation du sujet de recherche de Benjamin Scellier (partie orale de l'examen prédoctoral). Titre : Biologically Plausible Deep Learning Jury : Yoshua Bengio (superviseur), Ioannis Mitliagkas et Aaron Courville Lieu : AA 3195 Date: lundi le 11 décembre à 10h00. ========================================================= (english) Hello, You are invited to the predoc presentation of Benjamin Scellier. Title : Biologically Plausible Deep Learning Jury : Yoshua Bengio (superviseur), Ioannis Mitliagkas et Aaron Courville Location : AA 3195 Date: lundi le 11 décembre à 10h00. -- Aaron C. Courville Département d’Informatique et de recherche opérationnelle Université de Montréal email: Aaron.Courville(a)gmail.com

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Soutenance de thèse - PhD defense - David Warde-Farley
by Yoshua Bengio 05 Déc '17

05 Déc '17

Double feature next Monday - two PhD defenses: first David Warde-Farley, then Li Yao ------- Soutenance de thèse / PhD defense Lundi 11 décembre, local 3195 du Pavillon Aisenstadt 13h-15h Candidat: David Warde-Farley Directeur: Yoshua Bengio Président du jury: Emma Frejinger Examinateur externe: Graham Taylor Membre du jury: Simon Lacoste-Julien Feedforward architectures for classification and synthesis This thesis by articles makes several contributions to the field of deep learning, with applications to both classification and synthesis of natural images. Specifically, we introduce several new techniques for the construction and training of deep feed- forward networks, and present an empirical investigation into dropout, one of the most popular regularization strategies of the last several years. In the first article, we present a novel piece-wise linear parameterization of neural networks, maxout, which allows each hidden unit of a neural network to effectively learn its own convex activation function. We demonstrate improvements on several object recognition benchmarks, and empirically investigate the source of these improvements, including an improved synergy with the recently proposed dropout regularization method. In the second article, we further interrogate the dropout algorithm in particular. Focusing on networks of the popular rectified linear units (ReLU), we empirically examine several questions regarding dropout’s remarkable effectiveness as a regularizer, including questions surrounding the fast test-time rescaling trick and the geometric mean it approximates, interpretations as an ensemble as compared with traditional ensembles, and the importance of using a bagging-like criterion for optimization. In the third article, we address a practical problem in industrial-scale application of deep networks for multi-label object recognition, namely improving an existing model’s ability to discriminate between frequently confused classes. We accomplish this by using the network’s own predictions to inform a partitioning of the label space, and augment the network with dedicated discriminative capacity addressing each of the partitions. Finally, in the fourth article, we tackle the problem of fitting implicit generative models of open domain collections of natural images using the recently introduced Generative Adversarial Networks (GAN) paradigm. We introduce an augmented training procedure which employs a denoising autoencoder, trained in a high-level feature space learned by the discriminator, to guide the generator towards feature encodings which more closely resemble the data. We quantitatively evaluate our findings using the recently proposed Inception score.

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/ Li Yao
by Yoshua Bengio 05 Déc '17

05 Déc '17

Soutenance de thèse / PhD defense Lundi 11 décembre, local 3195 du Pavillon Aisenstadt Candidat: Li Yao Directeur: Yoshua Bengio Examinateur externe: Sanja Fidler Autres membres du jury: Aaron Courville, Christopher Pal Learning visual representations with neural networks for image generation and video captioning The past decade has been marked as a golden era of neural network research. Not only have neural networks been successfully applied to solve more and more challenging real-world problems, but also they have become the dominant approach in many of the places where they have been tested. These places include, for instance, language understanding, game playing, and computer vision, thanks to neural networks' superiority in computational efficiency and statistical capacity. This work applies neural networks to problems in computer vision where high-level and semantically meaningful representations play a fundamental role. It demonstrates both in theory and in experiment the ability to learn such representations from data with and without supervision. The main content of the work is divided into two parts. The first part studies neural networks in the context of learning visual representations for the task of video captioning. Models are developed to dynamically focus on different frames while generating a natural language description of a short video. Such a model is further improved by recurrent convolutional operations. The end of this part identifies fundamental challenges in video captioning and proposes a new type of model-based evaluation metric that may be used experimentally as an oracle to benchmark performance. The second part studies the family of models that generate images. While the first part is supervised, this part is unsupervised. The focus of it is the popular family of Neural Autoregressive Density Estimators (NADEs), a tractable probabilistic model for natural images. This work first makes a connection between NADEs and Generative Stochastic Networks (GSNs). The standard NADE is improved by introducing multiple iterations in its inference without increasing the number of parameters, which is dubbed "iterative NADEs".

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[Tea Talk] Romain Laroche (MS Maluuba) Fri Dec 1 10:30AM, AA6214
by Michael Noukhovitch 01 Déc '17

01 Déc '17

This week we have a researcher from MS Maluuba, *Romain Laroche,* giving a talk on *Friday Dec 1* at* 10:30AM* in room *AA6214*. See you there, this RL talk is sure to be rewarding! Michael *KEYWORDS* policy-based RL, bootstrapping, data/computational efficiency *TITLE *Safe Policy Improvement with Baseline Bootstrapping *ABSTRACT* A common goal in Reinforcement Learning is to derive a good strategy given a limited batch of data. In this paper, we propose a new strategy to compute a safe policy, guaranteed to perform at least as well as a given baseline strategy. We advocate that the assumptions made in previous work are too strong for real world applications and propose new algorithms allowing those assumptions to be satisfied only in a subset of the state-action pairs. While significantly relaxing the assumptions, our algorithms achieve the same accuracy guarantees than the previous work, and are also much more computationally efficient. We also show that the algorithms can be adapted to model-free Reinforcement Learning. *BIO* Graduated from Ecole Polytechnique in 2001 and then from Telecom PariTech in 2003, Romain Laroche joined the dialogue team at Orange in Paris, where he defended in 2010 a corporate PhD on Reinforcement Learning for industrial dialogue systems at Université Pierre et Marie Curie (Paris VI). Romain joined Maluuba in 2016, and is now a researcher at Microsoft Research Maluuba. During the past 6 years he supervised 5 PhDs, 3 postdocs, and a dozen of undergrad interns. Counting more than 40 papers at international conferences, his interest focuses now on Reinforcement Learning. His preferred application domains are dialogue systems (still his primary real-world motivation), Atari games (for benchmarking against other algorithms), and navigation toy problems (for empirical analysis and algorithm design).

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Lisa_seminaires Décembre 2017