Aaron Courville

sarvjeet-singh.ghotra@mila.quebec

Juan Duque

PhD - Université de Montréal

juan.duque@mila.quebec

PhD - Université de Montréal

udaykaran.kapur@mila.quebec

Arian Hosseini

PhD - Université de Montréal

Uday Kapur

PhD - Université de Montréal

Amr Khalifa

PhD - Université de Montréal

amr-khalifa.alshaykh@mila.quebec

alan.milligan@mila.quebec

Zhixuan Lin

PhD - Université de Montréal

PhD - Université de Montréal

Co-supervisor :

Pablo Samuel Castro

waltermayor@gmail.com

PhD - Université de Montréal

Principal supervisor :

Simon Lacoste-Julien

PhD - Université de Montréal

Principal supervisor :

Gauthier Gidel

andjela.mladenovic@mila.quebec

mohammed.muqeeth@mila.quebec

Muqeeth Mohammed

PhD - Université de Montréal

PhD - Université de Montréal

Co-supervisor :

Rishabh Agarwal

morgane.moss@mila.quebec

Andrei Nicolicioiu

PhD - Université de Montréal

andrei.nicolicioiu@gmail.com

michell.payanoperez@mila.quebec

Michael Noukhovitch

PhD - Université de Montréal

Johan Samir Obando Ceron

PhD - Université de Montréal

Co-supervisor :

Pablo Samuel Castro

johan.ceron@mila.quebec

PhD - Université de Montréal

Dereck Piché

PhD - Université de Montréal

pichedereck@gmail.com

khaled.rouissi@mila.quebec

Khaled Rouissi

Master's Research - Université de Montréal

Esra'a Saleh

PhD - Université de Montréal

Principal supervisor :

Glen Berseth

esraa.saleh@mila.quebec

Vedant Shah

PhD - Université de Montréal

vedant.shah@mila.quebec

colin.yiyang-lu@mila.quebec

Yusong Wu

PhD - Université de Montréal

Principal supervisor :

Anna (Cheng-Zhi) Huang

wu.yusong@mila.quebec

Colin Yiyang Lu

PhD - Université de Montréal

Sujin yun

PhD - Université de Montréal

sujin.yun@mila.quebec

xiaofeng.zhang@mila.quebec

Xiaofeng Zhang

PhD - Université de Montréal

Publications

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… (see more)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 (preprint)

arxiv.org

Feature-wise transformations

Harm Vries

2018-07-08

Distill (published)

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… (see more)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 (published)

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 … (see more)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 (unknown)

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… (see more)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

International Conference on Machine Learning (published)

proceedings.mlr.press

On the Spectral Bias of Deep Neural Networks

Nasim Rahaman

Felix Draxler

Fred Hamprecht

It is well known that over-parametrized deep neural networks (DNNs) are an overly expressive class of functions that can memorize even rando… (see more)m data with

2018-06-21

arXiv.org (preprint)

dblp.uni-trier.de

MINE: Mutual Information Neural Estimation

Ishmael Belghazi

Sai Rajeswar

Aristide Baratin

R Devon Hjelm

This paper presents a Mutual Information Neural Estimator (MINE) that is linearly scalable in dimensionality as well as in sample size. MINE… (see more) is back-propable and we prove that it is strongly consistent. We illustrate a handful of applications in which MINE is succesfully applied to enhance the property of generative models in both unsupervised and supervised settings. We apply our framework to estimate the information bottleneck, and apply it in tasks related to supervised classification problems. Our results demonstrate substantial added flexibility and improvement in these settings.

2018-05-03

ICLR.cc/2018/Conference (unknown)

Learning Visual Reasoning Without Strong Priors

We introduce a general-purpose conditioning method for neural networks called FiLM: Feature-wise Linear Modulation. FiLM layers influence ne… (see more)ural network computation via a simple, feature-wise affine transformation based on conditioning information. We show that FiLM layers are highly effective for visual reasoning - answering image-related questions which require a multi-step, high-level process - a task which has proven difficult for standard deep learning methods that do not explicitly model reasoning. Specifically, we show on visual reasoning tasks that FiLM layers 1) halve state-of-the-art error for the CLEVR benchmark, 2) modulate features in a coherent manner, 3) are robust to ablations and architectural modifications, and 4) generalize well to challenging, new data from few examples or even zero-shot.

2018-04-28

AAAI Conference on Artificial Intelligence (published)

arxiv.org

Generating Contradictory, Neutral, and Entailing Sentences

Learning distributed sentence representations remains an interesting problem in the field of Natural Language Processing (NLP). We want to l… (see more)earn a model that approximates the conditional latent space over the representations of a logical antecedent of the given statement. In our paper, we propose an approach to generating sentences, conditioned on an input sentence and a logical inference label. We do this by modeling the different possibilities for the output sentence as a distribution over the latent representation, which we train using an adversarial objective. We evaluate the model using two state-of-the-art models for the Recognizing Textual Entailment (RTE) task, and measure the BLEU scores against the actual sentences as a probe for the diversity of sentences produced by our model. The experiment results show that, given our framework, we have clear ways to improve the quality and diversity of generated sentences.

2018-03-06

ArXiv (preprint)

arxiv.org

Learning Generative Models with Locally Disentangled Latent Factors

Brady Neal

2018-02-14

(published)

Inferring Identity Factors for Grouped Examples

Shawn Tan

Christopher Pal

We propose a method for modelling groups of face images from the same identity. The model is trained to infer a distribution over the latent… (see more) space for identity given a small set of “training data”. One can then sample images using that latent representation to produce images of the same identity. We demonstrate that the model extracts disentangled factors for identity factors and image-specific vectors. We also perform generative classification over identities to assess its feasibility for few-shot face recognition.

2018-02-11

Neural Information Processing Systems (published)

Hierarchical Adversarially Learned Inference

Ishmael Belghazi

Sai Rajeswar

We propose a novel hierarchical generative model with a simple Markovian structure and a corresponding inference model. Both the generative … (see more)and inference model are trained using the adversarial learning paradigm. We demonstrate that the hierarchical structure supports the learning of progressively more abstract representations as well as providing semantically meaningful reconstructions with different levels of fidelity. Furthermore, we show that minimizing the Jensen-Shanon divergence between the generative and inference network is enough to minimize the reconstruction error. The resulting semantically meaningful hierarchical latent structure discovery is exemplified on the CelebA dataset. There, we show that the features learned by our model in an unsupervised way outperform the best handcrafted features. Furthermore, the extracted features remain competitive when compared to several recent deep supervised approaches on an attribute prediction task on CelebA. Finally, we leverage the model's inference network to achieve state-of-the-art performance on a semi-supervised variant of the MNIST digit classification task.

2018-02-03

ArXiv (preprint)