Aaron Courville

sarvjeet-singh.ghotra@mila.quebec

Juan Duque

Doctorat - UdeM

juan.duque@mila.quebec

Doctorat - UdeM

Doctorat - UdeM

Uday Kapur

Doctorat - UdeM

udaykaran.kapur@mila.quebec

Amr Khalifa

Doctorat - UdeM

amr-khalifa.alshaykh@mila.quebec

samuel.lavoie@mila.quebec

Samuel Lavoie

Doctorat - UdeM

Zhixuan Lin

Doctorat - UdeM

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Simon Lacoste-Julien

alan.milligan@mila.quebec

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Gauthier Gidel

andjela.mladenovic@mila.quebec

mohammed.muqeeth@mila.quebec

Google Scholar

Muqeeth Mohammed

Doctorat - UdeM

Doctorat - UdeM

Co-superviseur⋅e :

Rishabh Agarwal

morgane.moss@mila.quebec

Andrei Nicolicioiu

Doctorat - UdeM

andrei.nicolicioiu@gmail.com

Doctorat - UdeM

Johan Samir Obando Ceron

Doctorat - UdeM

Co-superviseur⋅e :

Pablo Samuel Castro

johan.ceron@mila.quebec

Doctorat - UdeM

michell.payanoperez@mila.quebec

Dereck Piché

Doctorat - UdeM

pichedereck@gmail.com

khaled.rouissi@mila.quebec

Khaled Rouissi

Maîtrise recherche - UdeM

Esra'a Saleh

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Glen Berseth

esraa.saleh@mila.quebec

colin.yiyang-lu@mila.quebec

Google Scholar

Vedant Shah

Doctorat - UdeM

vedant.shah@mila.quebec

Doctorat - UdeM

Yusong Wu

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Anna (Cheng-Zhi) Huang

wu.yusong@mila.quebec

Colin Yiyang Lu

Doctorat - UdeM

Sujin yun

Doctorat - UdeM

sujin.yun@mila.quebec

xiaofeng.zhang@mila.quebec

Google Scholar

Xiaofeng Zhang

Doctorat - UdeM

Publications

On Difficulties of Probability Distillation

Alexandre Lacoste

2018-09-26

(publié)

EnGAN: Latent Space MCMC and Maximum Entropy Generators for Energy-based Models

2018-09-26

(publié)

Pix2Scene: Learning Implicit 3D Representations from Images

Sai Rajeswar

Fahim Mannan

2018-09-26

(publié)

Unsupervised one-to-many image translation

Samuel Lavoie-Marchildon

R Devon Hjelm

2018-09-26

(publié)

W2GAN: RECOVERING AN OPTIMAL TRANSPORT MAP WITH A GAN

Leygonie Jacob*

Jennifer She*

Amjad Almahairi

Sai Rajeswar

2018-09-26

(publié)

On the Learning Dynamics of Deep Neural Networks

Remi Tachet des Combes

Mohammad Pezeshki

Samira Shabanian

Yoshua Bengio

While a lot of progress has been made in recent years, the dynamics of learning in deep nonlinear neural networks remain to this day largely… (voir plus) misunderstood. In this work, we study the case of binary classification and prove various properties of learning in such networks under strong assumptions such as linear separability of the data. Extending existing results from the linear case, we confirm empirical observations by proving that the classification error also follows a sigmoidal shape in nonlinear architectures. We show that given proper initialization, learning expounds parallel independent modes and that certain regions of parameter space might lead to failed training. We also demonstrate that input norm and features' frequency in the dataset lead to distinct convergence speeds which might shed some light on the generalization capabilities of deep neural networks. We provide a comparison between the dynamics of learning with cross-entropy and hinge losses, which could prove useful to understand recent progress in the training of generative adversarial networks. Finally, we identify a phenomenon that we baptize \textit{gradient starvation} where the most frequent features in a dataset prevent the learning of other less frequent but equally informative features.

2018-09-17

arXiv (prépublication)

arxiv.org

Approximate Exploration through State Abstraction

Adrien Ali Taiga

Bellemare Marc-Emmanuel

Although exploration in reinforcement learning is well understood from a theoretical point of view, provably correct methods remain impracti… (voir plus)cal. In this paper we study the interplay between exploration and approximation, what we call approximate exploration. Our main goal is to further our theoretical understanding of pseudo-count based exploration bonuses (Bellemare et al., 2016), a practical exploration scheme based on density modelling. As a warm-up, we quantify the performance of an exploration algorithm, MBIE-EB (Strehl and Littman, 2008), when explicitly combined with state aggregation. This allows us to confirm that, as might be expected, approximation allows the agent to trade off between learning speed and quality of the learned policy. Next, we show how a given density model can be related to an abstraction and that the corresponding pseudo-count bonus can act as a substitute in MBIE-EB combined with this abstraction, but may lead to either under- or over-exploration. Then, we show that a given density model also defines an implicit abstraction, and find a surprising mismatch between pseudo-counts derived either implicitly or explicitly. Finally we derive a new pseudo-count bonus alleviating this issue.

2018-08-28

ArXiv (prépublication)

arxiv.org

Feature-wise transformations

Harm Vries

2018-07-08

Distill (publié)

Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data

Amjad Almahairi

Sai Rajeswar

Alessandro Sordoni

Philip Bachman

Learning inter-domain mappings from unpaired data can improve performance in structured prediction tasks, such as image segmentation, by red… (voir plus)ucing the need for paired data. CycleGAN was recently proposed for this problem, but critically assumes the underlying inter-domain mapping is approximately deterministic and one-to-one. This assumption renders the model ineffective for tasks requiring flexible, many-to-many mappings. We propose a new model, called Augmented CycleGAN, which learns many-to-many mappings between domains. We examine Augmented CycleGAN qualitatively and quantitatively on several image datasets.

2018-07-02

Proceedings of the 35th International Conference on Machine Learning (publié)

proceedings.mlr.press

Mutual Information Neural Estimation

Mohamed Ishmael Belghazi

Sai Rajeswar

R Devon Hjelm

We argue that the estimation of mutual information between high dimensional continuous random variables can be achieved by gradient descent … (voir plus)over neural networks. We present a Mutual Information Neural Estimator (MINE) that is linearly scalable in dimensionality as well as in sample size, trainable through back-prop, and strongly consistent. We present a handful of applications on which MINE can be used to minimize or maximize mutual information. We apply MINE to improve adversarially trained generative models. We also use MINE to implement Information Bottleneck, applying it to supervised classification; our results demonstrate substantial improvement in flexibility and performance in these settings.

2018-07-02

International Conference on Machine Learning (inconnu)

proceedings.mlr.press

Neural Autoregressive Flows

Chin-wei Huang

David Krueger

Alexandre Lacoste

Normalizing flows and autoregressive models have been successfully combined to produce state-of-the-art results in density estimation, via M… (voir plus)asked Autoregressive Flows (MAF), and to accelerate state-of-the-art WaveNet-based speech synthesis to 20x faster than real-time, via Inverse Autoregressive Flows (IAF). We unify and generalize these approaches, replacing the (conditionally) affine univariate transformations of MAF/IAF with a more general class of invertible univariate transformations expressed as monotonic neural networks. We demonstrate that the proposed neural autoregressive flows (NAF) are universal approximators for continuous probability distributions, and their greater expressivity allows them to better capture multimodal target distributions. Experimentally, NAF yields state-of-the-art performance on a suite of density estimation tasks and outperforms IAF in variational autoencoders trained on binarized MNIST.

2018-07-02

Proceedings of the 35th International Conference on Machine Learning (publié)