Aaron Courville

Anirudh Buvanesh

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Laurent Charlin

anirudb1102@gmail.com

Razvan Ciuca

Maîtrise recherche - Université de Montréal

Alexandre Diz Ganito

Maîtrise recherche - UdeM

Juan Duque

Doctorat - UdeM

Doctorat - UdeM

Doctorat - UdeM

Uday Kapur

Maîtrise professionnelle - UdeM

Amr Khalifa

Doctorat - UdeM

andrei.nicolicioiu@gmail.com

Samuel Lavoie

Doctorat - UdeM

Zhixuan Lin

Doctorat - UdeM

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Doctorat - UdeM

Co-superviseur⋅e :

Rishabh Agarwal

Andrei Nicolicioiu

Doctorat - UdeM

Google Scholar

Evgenii Nikishin

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Co-superviseur⋅e :

Johan Samir Obando Ceron

Doctorat - UdeM

Co-superviseur⋅e :

Maîtrise recherche - UdeM

pichedereck@gmail.com

Esra'a Saleh

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Anna (Cheng-Zhi) Huang

Shawn Tan

Doctorat - UdeM

Doctorat - UdeM

Superviseur⋅e principal⋅e :

(Rex) Devon Hjelm

Google Scholar

Yusong Wu

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Anna (Cheng-Zhi) Huang

Xiaofeng Zhang

Doctorat - UdeM

Dinghuai Zhang

Doctorat - UdeM

Co-superviseur⋅e :

Hattie Zhou

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Hugo Larochelle

Publications

Neural Autoregressive Flows

Chin-Wei Huang

David Scott 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.

2018-07-03

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

proceedings.mlr.press

Straight to the Tree: Constituency Parsing with Neural Syntactic Distance

Yikang Shen

Zhouhan Lin

Athul Jacob

Alessandro Sordoni

In this work, we propose a novel constituency parsing scheme. The model first predicts a real-valued scalar, named syntactic distance, for e… (voir plus)ach split position in the sentence. The topology of grammar tree is then determined by the values of syntactic distances. Compared to traditional shift-reduce parsing schemes, our approach is free from the potentially disastrous compounding error. It is also easier to parallelize and much faster. Our model achieves the state-of-the-art single model F1 score of 92.1 on PTB and 86.4 on CTB dataset, which surpasses the previous single model results by a large margin.

2018-07-01

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (publié)

doi.org

On the Spectral Bias of Deep Neural Networks

Nasim Rahaman

Devansh Arpit

Aristide Baratin

Felix Draxler

Min Lin

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… (voir plus)m data with

2018-06-22

arXiv.org (prépublication)

dblp.uni-trier.de

Manifold Mixup: Encouraging Meaningful On-Manifold Interpolation as a Regularizer

Vikas Verma

Alex Lamb

Christopher Beckham

Ioannis Mitliagkas

Deep networks often perform well on the data manifold on which they are trained, yet give incorrect (and often very confident) answers when … (voir plus)evaluated on points from off of the training distribution. This is exemplified by the adversarial examples phenomenon but can also be seen in terms of model generalization and domain shift. We propose Manifold Mixup which encourages the network to produce more reasonable and less confident predictions at points with combinations of attributes not seen in the training set. This is accomplished by training on convex combinations of the hidden state representations of data samples. Using this method, we demonstrate improved semi-supervised learning, learning with limited labeled data, and robustness to adversarial examples. Manifold Mixup requires no (significant) additional computation. Analytical experiments on both real data and synthetic data directly support our hypothesis for why the Manifold Mixup method improves results.

2018-06-13

arXiv.org (prépublication)

dblp.uni-trier.de

MINE: Mutual Information Neural Estimation

Ishmael Belghazi

Sai Rajeswar

Aristide Baratin

(Rex) Devon Hjelm

This paper presents a Mutual Information Neural Estimator (MINE) that is linearly scalable in dimensionality as well as in sample size. MINE… (voir plus) 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.

2018-05-04

ICLR.cc/2018/Conference (inconnu)

FiLM: Visual Reasoning with a General Conditioning Layer

Ethan Perez

Florian Strub

Harm de Vries

Vincent Dumoulin

We introduce a general-purpose conditioning method for neural networks called FiLM: Feature-wise Linear Modulation. FiLM layers influence ne… (voir plus)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.

2018-04-29

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

doi.org

Generating Contradictory, Neutral, and Entailing Sentences

Yikang Shen

Shawn Tan

Chin-Wei Huang

Learning distributed sentence representations remains an interesting problem in the field of Natural Language Processing (NLP). We want to l… (voir plus)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.

2018-03-07

ArXiv (prépublication)

Learning Generative Models with Locally Disentangled Latent Factors

Brady Neal

Alex Lamb

Sherjil Ozair

One of the most successful techniques in generative models has been decomposing a complicated generation task into a series of simpler gener… (voir plus)ation tasks. For example, generating an image at a low resolution and then learning to refine that into a high resolution image often improves results substantially. Here we explore a novel strategy for decomposing generation for complicated objects in which we first generate latent variables which describe a subset of the observed variables, and then map from these latent variables to the observed space. We show that this allows us to achieve decoupled training of complicated generative models and present both theoretical and experimental results supporting the benefit of such an approach.

2018-02-15

(publié)

Inferring Identity Factors for Grouped Examples

Shawn Tan

Chris 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… (voir plus) 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.

2018-02-12

(publié)

Hierarchical Adversarially Learned Inference

Ishmael Belghazi

Sai Rajeswar

Olivier Mastropietro

Negar Rostamzadeh

Jovana Mitrovic

We propose a novel hierarchical generative model with a simple Markovian structure and a corresponding inference model. Both the generative … (voir plus)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.

2018-02-04

ArXiv (prépublication)

HoME: a Household Multimodal Environment

Simon Brodeur

Ethan Perez

Ankesh Anand

Florian Golemo

Luca Celotti

Florian Strub

Jean Rouat

Hugo Larochelle

We introduce HoME: a Household Multimodal Environment for artificial agents to learn from vision, audio, semantics, physics, and interaction… (voir plus) with objects and other agents, all within a realistic context. HoME integrates over 45,000 diverse 3D house layouts based on the SUNCG dataset, a scale which may facilitate learning, generalization, and transfer. HoME is an open-source, OpenAI Gym-compatible platform extensible to tasks in reinforcement learning, language grounding, sound-based navigation, robotics, multi-agent learning, and more. We hope HoME better enables artificial agents to learn as humans do: in an interactive, multimodal, and richly contextualized setting.

2018-01-01

ICLR (Workshop) (publié)

Improving Explorability in Variational Inference with Annealed Variational Objectives

Chin-Wei Huang

Shawn Tan

Alexandre Lacoste

Despite the advances in the representational capacity of approximate distributions for variational inference, the optimization process can s… (voir plus)till limit the density that is ultimately learned. We demonstrate the drawbacks of biasing the true posterior to be unimodal, and introduce Annealed Variational Objectives (AVO) into the training of hierarchical variational methods. Inspired by Annealed Importance Sampling, the proposed method facilitates learning by incorporating energy tempering into the optimization objective. In our experiments, we demonstrate our method's robustness to deterministic warm up, and the benefits of encouraging exploration in the latent space.