Aaron Courville

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Université de Montréal

Assistant Professor at University of Montreal

Pretraining Representations for Data-Efficient Reinforcement Learning

Data efficiency is a key challenge for deep reinforcement learning. We a...
06/09/2021 ∙ by Max Schwarzer, et al. ∙ 0 ∙ share

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Can Subnetwork Structure be the Key to Out-of-Distribution Generalization?

Can models with particular structure avoid being biased towards spurious...
06/05/2021 ∙ by Dinghuai Zhang, et al. ∙ 0 ∙ share

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A Variational Perspective on Diffusion-Based Generative Models and Score Matching

Discrete-time diffusion-based generative models and score matching metho...
06/05/2021 ∙ by Chin-Wei Huang, et al. ∙ 0 ∙ share

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Hierarchical Video Generation for Complex Data

Videos can often be created by first outlining a global description of t...
06/04/2021 ∙ by Lluis Castrejon, et al. ∙ 0 ∙ share

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Understanding by Understanding Not: Modeling Negation in Language Models

Negation is a core construction in natural language. Despite being very ...
05/07/2021 ∙ by Arian Hosseini, et al. ∙ 0 ∙ share

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Iterated learning for emergent systematicity in VQA

Although neural module networks have an architectural bias towards compo...
05/03/2021 ∙ by Ankit Vani, et al. ∙ 0 ∙ share

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Touch-based Curiosity for Sparse-Reward Tasks

Robots in many real-world settings have access to force/torque sensors i...
04/01/2021 ∙ by Sai Rajeswar, et al. ∙ 12 ∙ share

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Learning Task Decomposition with Ordered Memory Policy Network

Many complex real-world tasks are composed of several levels of sub-task...
03/19/2021 ∙ by Yuchen Lu, et al. ∙ 35 ∙ share

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Continuous Coordination As a Realistic Scenario for Lifelong Learning

Current deep reinforcement learning (RL) algorithms are still highly tas...
03/04/2021 ∙ by Hadi Nekoei, et al. ∙ 0 ∙ share

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Emergent Communication under Competition

The literature in modern machine learning has only negative results for ...
01/25/2021 ∙ by Michael Noukhovitch, et al. ∙ 0 ∙ share

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Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization

Flow-based models are powerful tools for designing probabilistic models ...
12/10/2020 ∙ by Chin-Wei Huang, et al. ∙ 0 ∙ share

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StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling

There are two major classes of natural language grammars – the dependenc...
12/01/2020 ∙ by Yikang Shen, et al. ∙ 2 ∙ share

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Gradient Starvation: A Learning Proclivity in Neural Networks

We identify and formalize a fundamental gradient descent phenomenon resu...
11/18/2020 ∙ by Mohammad Pezeshki, et al. ∙ 1 ∙ share

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Unsupervised Learning of Dense Visual Representations

Contrastive self-supervised learning has emerged as a promising approach...
11/11/2020 ∙ by Pedro O. Pinheiro, et al. ∙ 0 ∙ share

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NU-GAN: High resolution neural upsampling with GAN

In this paper, we propose NU-GAN, a new method for resampling audio from...
10/22/2020 ∙ by Rithesh Kumar, et al. ∙ 0 ∙ share

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Neural Approximate Sufficient Statistics for Implicit Models

We consider the fundamental problem of how to automatically construct su...
10/20/2020 ∙ by Yanzhi Chen, et al. ∙ 0 ∙ share

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Recursive Top-Down Production for Sentence Generation with Latent Trees

We model the recursive production property of context-free grammars for ...
10/09/2020 ∙ by Shawn Tan, et al. ∙ 0 ∙ share

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Supervised Seeded Iterated Learning for Interactive Language Learning

Language drift has been one of the major obstacles to train language mod...
10/06/2020 ∙ by Yuchen Lu, et al. ∙ 0 ∙ share

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Integrating Categorical Semantics into Unsupervised Domain Translation

While unsupervised domain translation (UDT) has seen a lot of success re...
10/03/2020 ∙ by Samuel Lavoie-Marchildon, et al. ∙ 0 ∙ share

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Data-Efficient Reinforcement Learning with Momentum Predictive Representations

While deep reinforcement learning excels at solving tasks where large am...
07/12/2020 ∙ by Max Schwarzer, et al. ∙ 0 ∙ share

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Generative Graph Perturbations for Scene Graph Prediction

Inferring objects and their relationships from an image is useful in man...
07/11/2020 ∙ by Boris Knyazev, et al. ∙ 0 ∙ share

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AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation

Entropy is ubiquitous in machine learning, but it is in general intracta...
06/09/2020 ∙ by Jae Hyun Lim, et al. ∙ 12 ∙ share

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Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation

Scene graph generation (SGG) aims to predict graph-structured descriptio...
05/17/2020 ∙ by Boris Knyazev, et al. ∙ 0 ∙ share

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A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM

We present Korbit, a large-scale, open-domain, mixed-interface, dialogue...
05/06/2020 ∙ by Iulian Vlad Serban, et al. ∙ 1 ∙ share

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Countering Language Drift with Seeded Iterated Learning

Supervised learning methods excel at capturing statistical properties of...
03/28/2020 ∙ by Yuchen Lu, et al. ∙ 1 ∙ share

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Pix2Shape – Towards Unsupervised Learning of 3D Scenes from Images using a View-based Representation

We infer and generate three-dimensional (3D) scene information from a si...
03/23/2020 ∙ by Sai Rajeswar, et al. ∙ 0 ∙ share

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Out-of-Distribution Generalization via Risk Extrapolation (REx)

Generalizing outside of the training distribution is an open challenge f...
03/02/2020 ∙ by David Krueger, et al. ∙ 25 ∙ share

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Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models

In this work, we propose a new family of generative flows on an augmente...
02/17/2020 ∙ by Chin-Wei Huang, et al. ∙ 0 ∙ share

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CLOSURE: Assessing Systematic Generalization of CLEVR Models

The CLEVR dataset of natural-looking questions about 3D-rendered scenes ...
12/12/2019 ∙ by Dzmitry Bahdanau, et al. ∙ 5 ∙ share

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Selective Brain Damage: Measuring the Disparate Impact of Model Pruning

Neural network pruning techniques have demonstrated it is possible to re...
11/13/2019 ∙ by Sara Hooker, et al. ∙ 20 ∙ share

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Ordered Memory

Stack-augmented recurrent neural networks (RNNs) have been of interest t...
10/29/2019 ∙ by Yikang Shen, et al. ∙ 0 ∙ share

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Icentia11K: An Unsupervised Representation Learning Dataset for Arrhythmia Subtype Discovery

We release the largest public ECG dataset of continuous raw signals for ...
10/21/2019 ∙ by Shawn Tan, et al. ∙ 12 ∙ share

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MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis

Previous works <cit.> have found that generating coherent raw audio wave...
10/08/2019 ∙ by Kundan Kumar, et al. ∙ 0 ∙ share

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No Press Diplomacy: Modeling Multi-Agent Gameplay

Diplomacy is a seven-player non-stochastic, non-cooperative game, where ...
09/04/2019 ∙ by Philip Paquette, et al. ∙ 4 ∙ share

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VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering

Embodied Question Answering (EQA) is a recently proposed task, where an ...
08/14/2019 ∙ by Cătălina Cangea, et al. ∙ 2 ∙ share

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Detecting semantic anomalies

We critically appraise the recent interest in out-of-distribution (OOD) ...
08/13/2019 ∙ by Faruk Ahmed, et al. ∙ 5 ∙ share

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Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment

This paper provides an empirical evaluation of recently developed explor...
08/06/2019 ∙ by Adrien Ali Taïga, et al. ∙ 2 ∙ share

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Adversarial Computation of Optimal Transport Maps

Computing optimal transport maps between high-dimensional and continuous...
06/24/2019 ∙ by Jacob Leygonie, et al. ∙ 0 ∙ share

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Investigating Biases in Textual Entailment Datasets

The ability to understand logical relationships between sentences is an ...
06/23/2019 ∙ by Shawn Tan, et al. ∙ 0 ∙ share

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Stochastic Neural Network with Kronecker Flow

Recent advances in variational inference enable the modelling of highly ...
06/10/2019 ∙ by Chin-Wei Huang, et al. ∙ 1 ∙ share

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Note on the bias and variance of variational inference

In this note, we study the relationship between the variational gap and ...
06/09/2019 ∙ by Chin-Wei Huang, et al. ∙ 0 ∙ share

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Batch weight for domain adaptation with mass shift

Unsupervised domain transfer is the task of transferring or translating ...
05/29/2019 ∙ by Mikołaj Bińkowski, et al. ∙ 0 ∙ share

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Hierarchical Importance Weighted Autoencoders

Importance weighted variational inference (Burda et al., 2015) uses mult...
05/13/2019 ∙ by Chin-Wei Huang, et al. ∙ 0 ∙ share

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Improved Conditional VRNNs for Video Prediction

Predicting future frames for a video sequence is a challenging generativ...
04/27/2019 ∙ by Lluis Castrejon, et al. ∙ 8 ∙ share

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Counterpoint by Convolution

Machine learning models of music typically break up the task of composit...
03/18/2019 ∙ by Cheng-Zhi Anna Huang, et al. ∙ 0 ∙ share

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Maximum Entropy Generators for Energy-Based Models

Unsupervised learning is about capturing dependencies between variables ...
01/24/2019 ∙ by Rithesh Kumar, et al. ∙ 2 ∙ share

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Deep Generative Modeling of LiDAR Data

Building models capable of generating structured output is a key challen...
12/04/2018 ∙ by Lucas Caccia, et al. ∙ 1 ∙ share

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

Numerous models for grounded language understanding have been recently p...
11/30/2018 ∙ by Dzmitry Bahdanau, et al. ∙ 6 ∙ share

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Planning in Dynamic Environments with Conditional Autoregressive Models

We demonstrate the use of conditional autoregressive generative models (...
11/25/2018 ∙ by Johanna Hansen, et al. ∙ 0 ∙ share

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Harmonic Recomposition using Conditional Autoregressive Modeling

We demonstrate a conditional autoregressive pipeline for efficient music...
11/18/2018 ∙ by Kyle Kastner, et al. ∙ 0 ∙ share

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Aaron Courville

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