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

Hierarchical Importance Weighted Autoencoders

Chin-wei Huang

Kris Sankaran

Eeshan Dhekane

Alexandre Lacoste

Importance weighted variational inference (Burda et al., 2015) uses multiple i.i.d. samples to have a tighter variational lower bound. We be… (see more)lieve a joint proposal has the potential of reducing the number of redundant samples, and introduce a hierarchical structure to induce correlation. The hope is that the proposals would coordinate to make up for the error made by one another to reduce the variance of the importance estimator. Theoretically, we analyze the condition under which convergence of the estimator variance can be connected to convergence of the lower bound. Empirically, we confirm that maximization of the lower bound does implicitly minimize variance. Further analysis shows that this is a result of negative correlation induced by the proposed hierarchical meta sampling scheme, and performance of inference also improves when the number of samples increases.

2019-05-23

International Conference on Machine Learning (published)

proceedings.mlr.press

Manifold Mixup: Better Representations by Interpolating Hidden States

Amir Najafi

David Lopez-Paz

Deep neural networks excel at learning the training data, but often provide incorrect and confident predictions when evaluated on slightly d… (see more)ifferent test examples. This includes distribution shifts, outliers, and adversarial examples. To address these issues, we propose Manifold Mixup, a simple regularizer that encourages neural networks to predict less confidently on interpolations of hidden representations. Manifold Mixup leverages semantic interpolations as additional training signal, obtaining neural networks with smoother decision boundaries at multiple levels of representation. As a result, neural networks trained with Manifold Mixup learn class-representations with fewer directions of variance. We prove theory on why this flattening happens under ideal conditions, validate it on practical situations, and connect it to previous works on information theory and generalization. In spite of incurring no significant computation and being implemented in a few lines of code, Manifold Mixup improves strong baselines in supervised learning, robustness to single-step adversarial attacks, and test log-likelihood.

2019-05-23

International Conference on Machine Learning (published)

proceedings.mlr.press

Representation Mixing for TTS Synthesis

Recent character and phoneme-based parametric TTS systems using deep learning have shown strong performance in natural speech generation. Ho… (see more)wever, the choice between character or phoneme input can create serious limitations for practical deployment, as direct control of pronunciation is crucial in certain cases. We demonstrate a simple method for combining multiple types of linguistic information in a single encoder, named representation mixing, enabling flexible choice between character, phoneme, or mixed representations during inference. Experiments and user studies on a public audiobook corpus show the efficacy of our approach.

2019-05-11

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (published)

Brief Report: Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks

Yikeng Shen

Shawn Tan

Alessandro Sordoni

2019-05-05

(published)

www.semanticscholar.org

Maximum Entropy Generators for Energy-Based Models

Maximum likelihood estimation of energy-based models is a challenging problem due to the intractability of the log-likelihood gradient. In t… (see more)his work, we propose learning both the energy function and an amortized approximate sampling mechanism using a neural generator network, which provides an efficient approximation of the log-likelihood gradient. The resulting objective requires maximizing entropy of the generated samples, which we perform using recently proposed nonparametric mutual information estimators. Finally, to stabilize the resulting adversarial game, we use a zero-centered gradient penalty derived as a necessary condition from the score matching literature. The proposed technique can generate sharp images with Inception and FID scores competitive with recent GAN techniques, does not suffer from mode collapse, and is competitive with state-of-the-art anomaly detection techniques.

2019-01-23

ArXiv (preprint)

MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis

Kundan Kumar

Rithesh Kumar

Thibault de Boissiere

Lucas Gestin

Wei Zhen Teoh

Jose Sotelo

Alexandre De Brébisson

Yoshua Bengio

2018-12-31

Neural Information Processing Systems (published)

Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks

Natural language is hierarchically structured: smaller units (e.g., phrases) are nested within larger units (e.g., clauses). When a larger c… (see more)onstituent ends, all of the smaller constituents that are nested within it must also be closed. While the standard LSTM architecture allows different neurons to track information at different time scales, it does not have an explicit bias towards modeling a hierarchy of constituents. This paper proposes to add such an inductive bias by ordering the neurons; a vector of master input and forget gates ensures that when a given neuron is updated, all the neurons that follow it in the ordering are also updated. Our novel recurrent architecture, ordered neurons LSTM (ON-LSTM), achieves good performance on four different tasks: language modeling, unsupervised parsing, targeted syntactic evaluation, and logical inference.

2018-12-31

ICLR.cc/2019/Conference (oral)

openreview.net

No Press Diplomacy: Modeling Multi-Agent Gameplay

Philip Paquette

Yuchen Lu

Steven Bocco

Max O. Smith

Satya Ortiz-Gagné

Jonathan K. Kummerfeld

Satinder Singh

Joelle Pineau

Diplomacy is a seven-player non-stochastic, non-cooperative game, where agents acquire resources through a mix of teamwork and betrayal. Rel… (see more)iance on trust and coordination makes Diplomacy the first non-cooperative multi-agent benchmark for complex sequential social dilemmas in a rich environment. In this work, we focus on training an agent that learns to play the No Press version of Diplomacy where there is no dedicated communication channel between players. We present DipNet, a neural-network-based policy model for No Press Diplomacy. The model was trained on a new dataset of more than 150,000 human games. Our model is trained by supervised learning (SL) from expert trajectories, which is then used to initialize a reinforcement learning (RL) agent trained through self-play. Both the SL and RL agents demonstrate state-of-the-art No Press performance by beating popular rule-based bots.

2018-12-31

Advances in Neural Information Processing Systems 32 (NeurIPS 2019) (published)

Systematic Generalization: What Is Required and Can It Be Learned?

Dzmitry Bahdanau*

Shikhar Murty*

2018-12-31

ICLR.cc/2019/Conference (poster)

openreview.net

On the Spectral Bias of Neural Networks

Nasim Rahaman

Felix Draxler

Fred A. Hamprecht

Neural networks are known to be a class of highly expressive functions able to fit even random input-output mappings with …

2018-12-31

International Conference on Machine Learning (published)

proceedings.mlr.press

Towards Jumpy Planning

Akilesh

Suriya Singh

Anirudh Goyal

Alexander Neitz

Model-free reinforcement learning (RL) is a powerful paradigm for learning complex tasks but suffers from high sample inefficiency as well a… (see more)s ignorance of the environment dynamics. On the other hand, a model-based RL agent learns dynamical causal models of the environment and uses them to plan. However, using a model at the scale of time-steps (usually tens of milliseconds) is mostly unfeasible in practice due to compounding prediction errors and computational requirements for making vast numbers of model queries during the planning process. We propose to use a modelbased planner together with a goal-conditioned policy trained with model-free learning. We use a model-based planner that operates at higher levels of abstraction i.e., decision states and use modelfree RL between the decision states. We validate our approach in terms of transfer and generalization performance and show that it leads to improvement over model-based planner that jumps to states that are fixed timesteps ahead.

2018-12-31

(published)

www.semanticscholar.org

Planning in Dynamic Environments with Conditional Autoregressive Models

Johanna Hansen

Kyle Kastner

Gregory Dudek

We demonstrate the use of conditional autoregressive generative models (van den Oord et al., 2016a) over a discrete latent space (van den Oo… (see more)rd et al., 2017b) for forward planning with MCTS. In order to test this method, we introduce a new environment featuring varying difficulty levels, along with moving goals and obstacles. The combination of high-quality frame generation and classical planning approaches nearly matches true environment performance for our task, demonstrating the usefulness of this method for model-based planning in dynamic environments.

2018-11-24

ArXiv (preprint)