Search Results for author: Aaron Courville

Found 171 papers, 101 papers with code

Unsupervised Dependency Graph Network

1 code implementation ACL 2022 Yikang Shen, Shawn Tan, Alessandro Sordoni, Peng Li, Jie zhou, Aaron Courville

We introduce a new model, the Unsupervised Dependency Graph Network (UDGN), that can induce dependency structures from raw corpora and the masked language modeling task.

Language Modelling Masked Language Modeling +2

The Primacy Bias in Deep Reinforcement Learning

1 code implementation16 May 2022 Evgenii Nikishin, Max Schwarzer, Pierluca D'Oro, Pierre-Luc Bacon, Aaron Courville

This work identifies a common flaw of deep reinforcement learning (RL) algorithms: a tendency to rely on early interactions and ignore useful evidence encountered later.

Atari Games 100k reinforcement-learning

Simplicial Embeddings in Self-Supervised Learning and Downstream Classification

1 code implementation1 Apr 2022 Samuel Lavoie, Christos Tsirigotis, Max Schwarzer, Kenji Kawaguchi, Ankit Vani, Aaron Courville

Specifically, we show that the temperature $\tau$ of the Softmax operation controls for the SEM representation's expressivity, allowing us to derive a tighter downstream classifier generalization bound than that for classifiers using unnormalized representations.

Classification Self-Supervised Learning

Generative Flow Networks for Discrete Probabilistic Modeling

1 code implementation3 Feb 2022 Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Volokhova, Aaron Courville, Yoshua Bengio

We present energy-based generative flow networks (EB-GFN), a novel probabilistic modeling algorithm for high-dimensional discrete data.

Invariant Representation Driven Neural Classifier for Anti-QCD Jet Tagging

no code implementations18 Jan 2022 Taoli Cheng, Aaron Courville

We leverage representation learning and the inductive bias in neural-net-based Standard Model jet classification tasks, to detect non-QCD signal jets.

Anomaly Detection Representation Learning

DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization

no code implementations ICLR 2022 Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron Courville, George Tucker, Sergey Levine

In this paper, we discuss how the implicit regularization effect of SGD seen in supervised learning could in fact be harmful in the offline deep RL setting, leading to poor generalization and degenerate feature representations.

Atari Games Offline RL +1

Multi-label Iterated Learning for Image Classification with Label Ambiguity

1 code implementation23 Nov 2021 Sai Rajeswar, Pau Rodriguez, Soumye Singhal, David Vazquez, Aaron Courville

We also show that MILe is effective reducing label noise, achieving state-of-the-art performance on real-world large-scale noisy data such as WebVision.

Image Classification Multi-Label Learning +1

Chunked Autoregressive GAN for Conditional Waveform Synthesis

1 code implementation ICLR 2022 Max Morrison, Rithesh Kumar, Kundan Kumar, Prem Seetharaman, Aaron Courville, Yoshua Bengio

We show that simple pitch and periodicity conditioning is insufficient for reducing this error relative to using autoregression.

Unifying Likelihood-free Inference with Black-box Optimization and Beyond

no code implementations ICLR 2022 Dinghuai Zhang, Jie Fu, Yoshua Bengio, Aaron Courville

Black-box optimization formulations for biological sequence design have drawn recent attention due to their promising potential impact on the pharmaceutical industry.

Drug Discovery

Learning to Dequantise with Truncated Flows

no code implementations ICLR 2022 Shawn Tan, Chin-wei Huang, Alessandro Sordoni, Aaron Courville

Addtionally, since the support of the marginal $q(z)$ is bounded and the support of prior $p(z)$ is not, we propose renormalising the prior distribution over the support of $q(z)$.

Variational Inference

Learnability and Expressiveness in Self-Supervised Learning

no code implementations29 Sep 2021 Yuchen Lu, Zhen Liu, Alessandro Sordoni, Aristide Baratin, Romain Laroche, Aaron Courville

In this work, we argue that representations induced by self-supervised learning (SSL) methods should both be expressive and learnable.

Data Augmentation Self-Supervised Learning

INFERNO: Inferring Object-Centric 3D Scene Representations without Supervision

no code implementations29 Sep 2021 Lluis Castrejon, Nicolas Ballas, Aaron Courville

Each object representation defines a localized neural radiance field that is used to generate 2D views of the scene through a differentiable rendering process.

Visual Reasoning

Inducing Reusable Skills From Demonstrations with Option-Controller Network

no code implementations29 Sep 2021 Siyuan Zhou, Yikang Shen, Yuchen Lu, Aaron Courville, Joshua B. Tenenbaum, Chuang Gan

With the isolation of information and the synchronous calling mechanism, we can impose a division of works between the controller and options in an end-to-end training regime.

Overcoming Label Ambiguity with Multi-label Iterated Learning

no code implementations29 Sep 2021 Sai Rajeswar Mudumba, Pau Rodriguez, Soumye Singhal, David Vazquez, Aaron Courville

This ambiguity biases models towards a single prediction, which could result in the suppression of classes that tend to co-occur in the data.

Multi-Label Learning Transfer Learning

On Bonus-Based Exploration Methods in the Arcade Learning Environment

no code implementations22 Sep 2021 Adrien Ali Taïga, William Fedus, Marlos C. Machado, Aaron Courville, Marc G. Bellemare

Research on exploration in reinforcement learning, as applied to Atari 2600 game-playing, has emphasized tackling difficult exploration problems such as Montezuma's Revenge (Bellemare et al., 2016).

Montezuma's Revenge

Deep Reinforcement Learning at the Edge of the Statistical Precipice

1 code implementation NeurIPS 2021 Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron Courville, Marc G. Bellemare

Most published results on deep RL benchmarks compare point estimates of aggregate performance such as mean and median scores across tasks, ignoring the statistical uncertainty implied by the use of a finite number of training runs.

reinforcement-learning

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

1 code implementation NeurIPS 2021 Chin-wei Huang, Jae Hyun Lim, Aaron Courville

Under this framework, we show that minimizing the score-matching loss is equivalent to maximizing a lower bound of the likelihood of the plug-in reverse SDE proposed by Song et al. (2021), bridging the theoretical gap.

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

no code implementations5 Jun 2021 Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, Aaron Courville

Can models with particular structure avoid being biased towards spurious correlation in out-of-distribution (OOD) generalization?

Out-of-Distribution Generalization

Hierarchical Video Generation for Complex Data

no code implementations4 Jun 2021 Lluis Castrejon, Nicolas Ballas, Aaron Courville

Inspired by this we propose a hierarchical model for video generation which follows a coarse to fine approach.

Video Generation

Understanding by Understanding Not: Modeling Negation in Language Models

1 code implementation NAACL 2021 Arian Hosseini, Siva Reddy, Dzmitry Bahdanau, R Devon Hjelm, Alessandro Sordoni, Aaron Courville

To improve language models in this regard, we propose to augment the language modeling objective with an unlikelihood objective that is based on negated generic sentences from a raw text corpus.

Language Modelling

Iterated learning for emergent systematicity in VQA

no code implementations ICLR 2021 Ankit Vani, Max Schwarzer, Yuchen Lu, Eeshan Dhekane, Aaron Courville

Although neural module networks have an architectural bias towards compositionality, they require gold standard layouts to generalize systematically in practice.

Question Answering Systematic Generalization +2

Touch-based Curiosity for Sparse-Reward Tasks

1 code implementation1 Apr 2021 Sai Rajeswar, Cyril Ibrahim, Nitin Surya, Florian Golemo, David Vazquez, Aaron Courville, Pedro O. Pinheiro

Robots in many real-world settings have access to force/torque sensors in their gripper and tactile sensing is often necessary in tasks that involve contact-rich motion.

Learning Task Decomposition with Ordered Memory Policy Network

no code implementations19 Mar 2021 Yuchen Lu, Yikang Shen, Siyuan Zhou, Aaron Courville, Joshua B. Tenenbaum, Chuang Gan

The discovered subtask hierarchy could be used to perform task decomposition, recovering the subtask boundaries in an unstruc-tured demonstration.

Emergent Communication under Competition

1 code implementation25 Jan 2021 Michael Noukhovitch, Travis LaCroix, Angeliki Lazaridou, Aaron Courville

First, we show that communication is proportional to cooperation, and it can occur for partially competitive scenarios using standard learning algorithms.

Systematic generalisation with group invariant predictions

no code implementations ICLR 2021 Faruk Ahmed, Yoshua Bengio, Harm van Seijen, Aaron Courville

We consider situations where the presence of dominant simpler correlations with the target variable in a training set can cause an SGD-trained neural network to be less reliant on more persistently-correlating complex features.

SSW-GAN: Scalable Stage-wise Training of Video GANs

no code implementations1 Jan 2021 Lluis Castrejon, Nicolas Ballas, Aaron Courville

Current state-of-the-art generative models for videos have high computational requirements that impede high resolution generations beyond a few frames.

Neural Approximate Sufficient Statistics for Likelihood-free Inference

no code implementations ICLR 2021 Yanzhi Chen, Dinghuai Zhang, Michael U. Gutmann, Aaron Courville, Zhanxing Zhu

We consider the fundamental problem of how to automatically construct summary statistics for likelihood-free inference where the evaluation of likelihood function is intractable but sampling / simulating data from the model is possible.

Frame

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

1 code implementation ACL 2021 Yikang Shen, Yi Tay, Che Zheng, Dara Bahri, Donald Metzler, Aaron Courville

There are two major classes of natural language grammar -- the dependency grammar that models one-to-one correspondences between words and the constituency grammar that models the assembly of one or several corresponded words.

Constituency Parsing Language Modelling +2

Bijective-Contrastive Estimation

no code implementations pproximateinference AABI Symposium 2021 Jae Hyun Lim, Chin-wei Huang, Aaron Courville, Christopher Pal

In this work, we propose Bijective-Contrastive Estimation (BCE), a classification-based learning criterion for energy-based models.

Classification

Gradient Starvation: A Learning Proclivity in Neural Networks

2 code implementations NeurIPS 2021 Mohammad Pezeshki, Sékou-Oumar Kaba, Yoshua Bengio, Aaron Courville, Doina Precup, Guillaume Lajoie

We identify and formalize a fundamental gradient descent phenomenon resulting in a learning proclivity in over-parameterized neural networks.

NU-GAN: High resolution neural upsampling with GAN

no code implementations22 Oct 2020 Rithesh Kumar, Kundan Kumar, Vicki Anand, Yoshua Bengio, Aaron Courville

In this paper, we propose NU-GAN, a new method for resampling audio from lower to higher sampling rates (upsampling).

Audio Generation Speech Synthesis

Neural Approximate Sufficient Statistics for Implicit Models

no code implementations20 Oct 2020 Yanzhi Chen, Dinghuai Zhang, Michael Gutmann, Aaron Courville, Zhanxing Zhu

We consider the fundamental problem of how to automatically construct summary statistics for implicit generative models where the evaluation of the likelihood function is intractable, but sampling data from the model is possible.

Frame

Integrating Categorical Semantics into Unsupervised Domain Translation

1 code implementation ICLR 2021 Samuel Lavoie, Faruk Ahmed, Aaron Courville

While unsupervised domain translation (UDT) has seen a lot of success recently, we argue that mediating its translation via categorical semantic features could broaden its applicability.

Translation

Data-Efficient Reinforcement Learning with Self-Predictive Representations

1 code implementation ICLR 2021 Max Schwarzer, Ankesh Anand, Rishab Goel, R. Devon Hjelm, Aaron Courville, Philip Bachman

We further improve performance by adding data augmentation to the future prediction loss, which forces the agent's representations to be consistent across multiple views of an observation.

Atari Games 100k Data Augmentation +4

AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation

2 code implementations ICML 2020 Jae Hyun Lim, Aaron Courville, Christopher Pal, Chin-wei Huang

Entropy is ubiquitous in machine learning, but it is in general intractable to compute the entropy of the distribution of an arbitrary continuous random variable.

Continuous Control Denoising +1

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

1 code implementation17 May 2020 Boris Knyazev, Harm de Vries, Cătălina Cangea, Graham W. Taylor, Aaron Courville, Eugene Belilovsky

We show that such models can suffer the most in their ability to generalize to rare compositions, evaluating two different models on the Visual Genome dataset and its more recent, improved version, GQA.

Graph Generation Scene Graph Generation

Countering Language Drift with Seeded Iterated Learning

no code implementations ICML 2020 Yuchen Lu, Soumye Singhal, Florian Strub, Olivier Pietquin, Aaron Courville

At each time step, the teacher is created by copying the student agent, before being finetuned to maximize task completion.

Translation

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

1 code implementation23 Mar 2020 Sai Rajeswar, Fahim Mannan, Florian Golemo, Jérôme Parent-Lévesque, David Vazquez, Derek Nowrouzezahrai, Aaron Courville

We propose Pix2Shape, an approach to solve this problem with four components: (i) an encoder that infers the latent 3D representation from an image, (ii) a decoder that generates an explicit 2. 5D surfel-based reconstruction of a scene from the latent code (iii) a differentiable renderer that synthesizes a 2D image from the surfel representation, and (iv) a critic network trained to discriminate between images generated by the decoder-renderer and those from a training distribution.

Solving ODE with Universal Flows: Approximation Theory for Flow-Based Models

no code implementations ICLR Workshop DeepDiffEq 2019 Chin-wei Huang, Laurent Dinh, Aaron Courville

Normalizing flows are powerful invertible probabilistic models that can be used to translate two probability distributions, in a way that allows us to efficiently track the change of probability density.

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

1 code implementation17 Feb 2020 Chin-wei Huang, Laurent Dinh, Aaron Courville

In this work, we propose a new family of generative flows on an augmented data space, with an aim to improve expressivity without drastically increasing the computational cost of sampling and evaluation of a lower bound on the likelihood.

Image Generation

On Bonus Based Exploration Methods In The Arcade Learning Environment

no code implementations ICLR 2020 Adrien Ali Taiga, William Fedus, Marlos C. Machado, Aaron Courville, Marc G. Bellemare

Research on exploration in reinforcement learning, as applied to Atari 2600 game-playing, has emphasized tackling difficult exploration problems such as Montezuma's Revenge (Bellemare et al., 2016).

Montezuma's Revenge

CLOSURE: Assessing Systematic Generalization of CLEVR Models

3 code implementations12 Dec 2019 Dzmitry Bahdanau, Harm de Vries, Timothy J. O'Donnell, Shikhar Murty, Philippe Beaudoin, Yoshua Bengio, Aaron Courville

In this work, we study how systematic the generalization of such models is, that is to which extent they are capable of handling novel combinations of known linguistic constructs.

Few-Shot Learning Systematic Generalization +1

What Do Compressed Deep Neural Networks Forget?

2 code implementations13 Nov 2019 Sara Hooker, Aaron Courville, Gregory Clark, Yann Dauphin, Andrea Frome

However, this measure of performance conceals significant differences in how different classes and images are impacted by model compression techniques.

Fairness Interpretability Techniques for Deep Learning +4

Ordered Memory

1 code implementation NeurIPS 2019 Yikang Shen, Shawn Tan, Arian Hosseini, Zhouhan Lin, Alessandro Sordoni, Aaron Courville

Inspired by Ordered Neurons (Shen et al., 2018), we introduce a new attention-based mechanism and use its cumulative probability to control the writing and erasing operation of the memory.

Icentia11K: An Unsupervised Representation Learning Dataset for Arrhythmia Subtype Discovery

1 code implementation21 Oct 2019 Shawn Tan, Guillaume Androz, Ahmad Chamseddine, Pierre Fecteau, Aaron Courville, Yoshua Bengio, Joseph Paul Cohen

We release the largest public ECG dataset of continuous raw signals for representation learning containing 11 thousand patients and 2 billion labelled beats.

Representation Learning

MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis

21 code implementations NeurIPS 2019 Kundan Kumar, Rithesh Kumar, Thibault de Boissiere, Lucas Gestin, Wei Zhen Teoh, Jose Sotelo, Alexandre de Brebisson, Yoshua Bengio, Aaron Courville

In this paper, we show that it is possible to train GANs reliably to generate high quality coherent waveforms by introducing a set of architectural changes and simple training techniques.

Speech Synthesis Translation

{COMPANYNAME}11K: An Unsupervised Representation Learning Dataset for Arrhythmia Subtype Discovery

no code implementations25 Sep 2019 Shawn Tan, Guillaume Androz, Ahmad Chamseddine, Pierre Fecteau, Aaron Courville, Yoshua Bengio, Joseph Paul Cohen

We release the largest public ECG dataset of continuous raw signals for representation learning containing over 11k patients and 2 billion labelled beats.

Representation Learning

Selective Brain Damage: Measuring the Disparate Impact of Model Pruning

no code implementations25 Sep 2019 Sara Hooker, Yann Dauphin, Aaron Courville, Andrea Frome

Neural network pruning techniques have demonstrated it is possible to remove the majority of weights in a network with surprisingly little degradation to top-1 test set accuracy.

Network Pruning

Selfish Emergent Communication

no code implementations25 Sep 2019 Michael Noukhovitch, Travis LaCroix, Aaron Courville

Current literature in machine learning holds that unaligned, self-interested agents do not learn to use an emergent communication channel.

No Press Diplomacy: Modeling Multi-Agent Gameplay

1 code implementation4 Sep 2019 Philip Paquette, Yuchen Lu, Steven Bocco, Max O. Smith, Satya Ortiz-Gagne, Jonathan K. Kummerfeld, Satinder Singh, Joelle Pineau, Aaron Courville

Diplomacy is a seven-player non-stochastic, non-cooperative game, where agents acquire resources through a mix of teamwork and betrayal.

VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering

1 code implementation14 Aug 2019 Cătălina Cangea, Eugene Belilovsky, Pietro Liò, Aaron Courville

The goal of this dataset is to assess question-answering performance from nearly-ideal navigation paths, while considering a much more complete variety of questions than current instantiations of the EQA task.

Embodied Question Answering Question Answering +2

Detecting semantic anomalies

1 code implementation13 Aug 2019 Faruk Ahmed, Aaron Courville

We critically appraise the recent interest in out-of-distribution (OOD) detection and question the practical relevance of existing benchmarks.

Anomaly Detection Multi-Task Learning +2

Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment

no code implementations6 Aug 2019 Adrien Ali Taïga, William Fedus, Marlos C. Machado, Aaron Courville, Marc G. Bellemare

This paper provides an empirical evaluation of recently developed exploration algorithms within the Arcade Learning Environment (ALE).

Montezuma's Revenge

Adversarial Computation of Optimal Transport Maps

1 code implementation24 Jun 2019 Jacob Leygonie, Jennifer She, Amjad Almahairi, Sai Rajeswar, Aaron Courville

We show that during training, our generator follows the $W_2$-geodesic between the initial and the target distributions.

Investigating Biases in Textual Entailment Datasets

no code implementations23 Jun 2019 Shawn Tan, Yikang Shen, Chin-wei Huang, Aaron Courville

The ability to understand logical relationships between sentences is an important task in language understanding.

Natural Language Inference Question Answering +2

Stochastic Neural Network with Kronecker Flow

no code implementations10 Jun 2019 Chin-wei Huang, Ahmed Touati, Pascal Vincent, Gintare Karolina Dziugaite, Alexandre Lacoste, Aaron Courville

Recent advances in variational inference enable the modelling of highly structured joint distributions, but are limited in their capacity to scale to the high-dimensional setting of stochastic neural networks.

Multi-Armed Bandits Variational Inference

Note on the bias and variance of variational inference

1 code implementation9 Jun 2019 Chin-wei Huang, Aaron Courville

In this note, we study the relationship between the variational gap and the variance of the (log) likelihood ratio.

Variational Inference

Batch weight for domain adaptation with mass shift

no code implementations29 May 2019 Mikołaj Bińkowski, R. Devon Hjelm, Aaron Courville

We also provide rigorous probabilistic setting for domain transfer and new simplified objective for training transfer networks, an alternative to complex, multi-component loss functions used in the current state-of-the art image-to-image translation models.

Domain Adaptation Image-to-Image Translation +1

Hierarchical Importance Weighted Autoencoders

1 code implementation13 May 2019 Chin-wei Huang, Kris Sankaran, Eeshan Dhekane, Alexandre Lacoste, Aaron Courville

We believe a joint proposal has the potential of reducing the number of redundant samples, and introduce a hierarchical structure to induce correlation.

Variational Inference

Pix2Scene: Learning Implicit 3D Representations from Images

no code implementations ICLR 2019 Sai Rajeswar, Fahim Mannan, Florian Golemo, David Vazquez, Derek Nowrouzezahrai, Aaron Courville

Modelling 3D scenes from 2D images is a long-standing problem in computer vision with implications in, e. g., simulation and robotics.

Manifold Mixup: Learning Better Representations by Interpolating Hidden States

1 code implementation ICLR 2019 Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Aaron Courville, Ioannis Mitliagkas, Yoshua Bengio

Because the hidden states are learned, this has an important effect of encouraging the hidden states for a class to be concentrated in such a way so that interpolations within the same class or between two different classes do not intersect with the real data points from other classes.

Unsupervised one-to-many image translation

no code implementations ICLR 2019 Samuel Lavoie-Marchildon, Sebastien Lachapelle, Mikołaj Bińkowski, Aaron Courville, Yoshua Bengio, R. Devon Hjelm

We perform completely unsupervised one-sided image to image translation between a source domain $X$ and a target domain $Y$ such that we preserve relevant underlying shared semantics (e. g., class, size, shape, etc).

Translation Unsupervised Image-To-Image Translation

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

no code implementations ICLR 2019 Rithesh Kumar, Anirudh Goyal, Aaron Courville, Yoshua Bengio

Unsupervised learning is about capturing dependencies between variables and is driven by the contrast between the probable vs improbable configurations of these variables, often either via a generative model which only samples probable ones or with an energy function (unnormalized log-density) which is low for probable ones and high for improbable ones.

Anomaly Detection

Improved Conditional VRNNs for Video Prediction

1 code implementation ICCV 2019 Lluis Castrejon, Nicolas Ballas, Aaron Courville

To address this issue, we propose to increase the expressiveness of the latent distributions and to use higher capacity likelihood models.

Video Generation Video Prediction

Counterpoint by Convolution

3 code implementations18 Mar 2019 Cheng-Zhi Anna Huang, Tim Cooijmans, Adam Roberts, Aaron Courville, Douglas Eck

Machine learning models of music typically break up the task of composition into a chronological process, composing a piece of music in a single pass from beginning to end.

Music Generation Music Modeling

Maximum Entropy Generators for Energy-Based Models

2 code implementations24 Jan 2019 Rithesh Kumar, Sherjil Ozair, Anirudh Goyal, Aaron Courville, Yoshua Bengio

Maximum likelihood estimation of energy-based models is a challenging problem due to the intractability of the log-likelihood gradient.

Anomaly Detection

Deep Generative Modeling of LiDAR Data

1 code implementation4 Dec 2018 Lucas Caccia, Herke van Hoof, Aaron Courville, Joelle Pineau

In this work, we show that one can adapt deep generative models for this task by unravelling lidar scans into a 2D point map.

Point Cloud Generation

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

2 code implementations ICLR 2019 Dzmitry Bahdanau, Shikhar Murty, Michael Noukhovitch, Thien Huu Nguyen, Harm de Vries, Aaron Courville

Numerous models for grounded language understanding have been recently proposed, including (i) generic models that can be easily adapted to any given task and (ii) intuitively appealing modular models that require background knowledge to be instantiated.

Systematic Generalization Visual Question Answering +1

Planning in Dynamic Environments with Conditional Autoregressive Models

1 code implementation25 Nov 2018 Johanna Hansen, Kyle Kastner, Aaron Courville, Gregory Dudek

We demonstrate the use of conditional autoregressive generative models (van den Oord et al., 2016a) over a discrete latent space (van den Oord et al., 2017b) for forward planning with MCTS.

Frame

Harmonic Recomposition using Conditional Autoregressive Modeling

1 code implementation18 Nov 2018 Kyle Kastner, Rithesh Kumar, Tim Cooijmans, Aaron Courville

We demonstrate a conditional autoregressive pipeline for efficient music recomposition, based on methods presented in van den Oord et al.(2017).

Representation Mixing for TTS Synthesis

no code implementations17 Nov 2018 Kyle Kastner, João Felipe Santos, Yoshua Bengio, Aaron Courville

Recent character and phoneme-based parametric TTS systems using deep learning have shown strong performance in natural speech generation.

On Difficulties of Probability Distillation

no code implementations27 Sep 2018 Chin-wei Huang, Faruk Ahmed, Kundan Kumar, Alexandre Lacoste, Aaron Courville

Probability distillation has recently been of interest to deep learning practitioners as it presents a practical solution for sampling from autoregressive models for deployment in real-time applications.

Convergence Properties of Deep Neural Networks on Separable Data

no code implementations27 Sep 2018 Remi Tachet des Combes, Mohammad Pezeshki, Samira Shabanian, Aaron Courville, 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 misunderstood.

W2GAN: RECOVERING AN OPTIMAL TRANSPORT MAP WITH A GAN

no code implementations27 Sep 2018 Leygonie Jacob*, Jennifer She*, Amjad Almahairi, Sai Rajeswar, Aaron Courville

In this work we address the converse question: is it possible to recover an optimal map in a GAN fashion?

On the Learning Dynamics of Deep Neural Networks

no code implementations18 Sep 2018 Remi Tachet, Mohammad Pezeshki, Samira Shabanian, Aaron Courville, 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 misunderstood.

General Classification

Improving Explorability in Variational Inference with Annealed Variational Objectives

1 code implementation NeurIPS 2018 Chin-wei Huang, Shawn Tan, Alexandre Lacoste, Aaron Courville

Despite the advances in the representational capacity of approximate distributions for variational inference, the optimization process can still limit the density that is ultimately learned.

Variational Inference

Approximate Exploration through State Abstraction

no code implementations29 Aug 2018 Adrien Ali Taïga, Aaron Courville, Marc G. Bellemare

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.

Visual Reasoning with Multi-hop Feature Modulation

1 code implementation ECCV 2018 Florian Strub, Mathieu Seurin, Ethan Perez, Harm de Vries, Jérémie Mary, Philippe Preux, Aaron Courville, Olivier Pietquin

Recent breakthroughs in computer vision and natural language processing have spurred interest in challenging multi-modal tasks such as visual question-answering and visual dialogue.

Question Answering Visual Dialog +2

Mutual Information Neural Estimation

no code implementations ICML 2018 Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, Devon Hjelm

We argue that the estimation of mutual information between high dimensional continuous random variables can be achieved by gradient descent over neural networks.

General Classification

On the Spectral Bias of Neural Networks

2 code implementations ICLR 2019 Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred A. Hamprecht, Yoshua Bengio, Aaron Courville

Neural networks are known to be a class of highly expressive functions able to fit even random input-output mappings with $100\%$ accuracy.

Learning Distributed Representations from Reviews for Collaborative Filtering

no code implementations18 Jun 2018 Amjad Almahairi, Kyle Kastner, Kyunghyun Cho, Aaron Courville

However, interestingly, the greater modeling power offered by the recurrent neural network appears to undermine the model's ability to act as a regularizer of the product representations.

Collaborative Filtering Recommendation Systems

Manifold Mixup: Better Representations by Interpolating Hidden States

11 code implementations ICLR 2019 Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, Aaron Courville, David Lopez-Paz, Yoshua Bengio

Deep neural networks excel at learning the training data, but often provide incorrect and confident predictions when evaluated on slightly different test examples.

Image Classification

Neural Autoregressive Flows

4 code implementations ICML 2018 Chin-wei Huang, David Krueger, Alexandre Lacoste, Aaron Courville

Normalizing flows and autoregressive models have been successfully combined to produce state-of-the-art results in density estimation, via Masked 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).

Density Estimation Speech Synthesis

Generating Contradictory, Neutral, and Entailing Sentences

no code implementations7 Mar 2018 Yikang Shen, Shawn Tan, Chin-wei Huang, Aaron Courville

Learning distributed sentence representations remains an interesting problem in the field of Natural Language Processing (NLP).

Natural Language Inference

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

3 code implementations ICML 2018 Amjad Almahairi, Sai Rajeswar, Alessandro Sordoni, Philip Bachman, Aaron Courville

Learning inter-domain mappings from unpaired data can improve performance in structured prediction tasks, such as image segmentation, by reducing the need for paired data.

Semantic Segmentation Structured Prediction

Hierarchical Adversarially Learned Inference

no code implementations ICLR 2018 Mohamed Ishmael Belghazi, Sai Rajeswar, Olivier Mastropietro, Negar Rostamzadeh, Jovana Mitrovic, Aaron Courville

We propose a novel hierarchical generative model with a simple Markovian structure and a corresponding inference model.

MINE: Mutual Information Neural Estimation

18 code implementations12 Jan 2018 Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeswar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, R. Devon Hjelm

We argue that the estimation of mutual information between high dimensional continuous random variables can be achieved by gradient descent over neural networks.

General Classification

Learning Generative Models with Locally Disentangled Latent Factors

no code implementations ICLR 2018 Brady Neal, Alex Lamb, Sherjil Ozair, Devon Hjelm, Aaron Courville, Yoshua Bengio, Ioannis Mitliagkas

One of the most successful techniques in generative models has been decomposing a complicated generation task into a series of simpler generation tasks.

GibbsNet: Iterative Adversarial Inference for Deep Graphical Models

no code implementations NeurIPS 2017 Alex Lamb, Devon Hjelm, Yaroslav Ganin, Joseph Paul Cohen, Aaron Courville, Yoshua Bengio

Directed latent variable models that formulate the joint distribution as $p(x, z) = p(z) p(x \mid z)$ have the advantage of fast and exact sampling.

HoME: a Household Multimodal Environment

no code implementations29 Nov 2017 Simon Brodeur, Ethan Perez, Ankesh Anand, Florian Golemo, Luca Celotti, Florian Strub, Jean Rouat, Hugo Larochelle, Aaron Courville

We introduce HoME: a Household Multimodal Environment for artificial agents to learn from vision, audio, semantics, physics, and interaction with objects and other agents, all within a realistic context.

OpenAI Gym reinforcement-learning

Neural Language Modeling by Jointly Learning Syntax and Lexicon

1 code implementation ICLR 2018 Yikang Shen, Zhouhan Lin, Chin-wei Huang, Aaron Courville

In this paper, We propose a novel neural language model, called the Parsing-Reading-Predict Networks (PRPN), that can simultaneously induce the syntactic structure from unannotated sentences and leverage the inferred structure to learn a better language model.

Ranked #11 on Constituency Grammar Induction on PTB (Max F1 (WSJ) metric)

Constituency Grammar Induction Language Modelling

Learnable Explicit Density for Continuous Latent Space and Variational Inference

no code implementations6 Oct 2017 Chin-wei Huang, Ahmed Touati, Laurent Dinh, Michal Drozdzal, Mohammad Havaei, Laurent Charlin, Aaron Courville

In this paper, we study two aspects of the variational autoencoder (VAE): the prior distribution over the latent variables and its corresponding posterior.

Density Estimation Variational Inference

Self-organized Hierarchical Softmax

no code implementations26 Jul 2017 Yikang Shen, Shawn Tan, Chrisopher Pal, Aaron Courville

We propose a new self-organizing hierarchical softmax formulation for neural-network-based language models over large vocabularies.

Language Modelling Sentence Compression

Learning Visual Reasoning Without Strong Priors

2 code implementations10 Jul 2017 Ethan Perez, Harm de Vries, Florian Strub, Vincent Dumoulin, Aaron Courville

Previous work has operated under the assumption that visual reasoning calls for a specialized architecture, but we show that a general architecture with proper conditioning can learn to visually reason effectively.

Visual Reasoning

Adversarial Generation of Natural Language

no code implementations WS 2017 Sai Rajeswar, Sandeep Subramanian, Francis Dutil, Christopher Pal, Aaron Courville

Generative Adversarial Networks (GANs) have gathered a lot of attention from the computer vision community, yielding impressive results for image generation.

Image Generation Language Modelling

End-to-end optimization of goal-driven and visually grounded dialogue systems

2 code implementations15 Mar 2017 Florian Strub, Harm de Vries, Jeremie Mary, Bilal Piot, Aaron Courville, Olivier Pietquin

End-to-end design of dialogue systems has recently become a popular research topic thanks to powerful tools such as encoder-decoder architectures for sequence-to-sequence learning.

Dialogue Management Visual Question Answering

Calibrating Energy-based Generative Adversarial Networks

1 code implementation6 Feb 2017 Zihang Dai, Amjad Almahairi, Philip Bachman, Eduard Hovy, Aaron Courville

In this paper, we propose to equip Generative Adversarial Networks with the ability to produce direct energy estimates for samples. Specifically, we propose a flexible adversarial training framework, and prove this framework not only ensures the generator converges to the true data distribution, but also enables the discriminator to retain the density information at the global optimal.

Image Generation

Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks

1 code implementation10 Jan 2017 Ying Zhang, Mohammad Pezeshki, Philemon Brakel, Saizheng Zhang, Cesar Laurent Yoshua Bengio, Aaron Courville

Meanwhile, Connectionist Temporal Classification (CTC) with Recurrent Neural Networks (RNNs), which is proposed for labeling unsegmented sequences, makes it feasible to train an end-to-end speech recognition system instead of hybrid settings.

Automatic Speech Recognition

SampleRNN: An Unconditional End-to-End Neural Audio Generation Model

3 code implementations22 Dec 2016 Soroush Mehri, Kundan Kumar, Ishaan Gulrajani, Rithesh Kumar, Shubham Jain, Jose Sotelo, Aaron Courville, Yoshua Bengio

In this paper we propose a novel model for unconditional audio generation based on generating one audio sample at a time.

Audio Generation

Generalizable Features From Unsupervised Learning

no code implementations12 Dec 2016 Mehdi Mirza, Aaron Courville, Yoshua Bengio

In this work, we explore the potential of unsupervised learning to find features that promote better generalization to settings outside the supervised training distribution.

Piecewise Latent Variables for Neural Variational Text Processing

2 code implementations EMNLP (ACL) 2017 Iulian V. Serban, Alexander G. Ororbia II, Joelle Pineau, Aaron Courville

Advances in neural variational inference have facilitated the learning of powerful directed graphical models with continuous latent variables, such as variational autoencoders.

Text Generation Variational Inference

GuessWhat?! Visual object discovery through multi-modal dialogue

3 code implementations CVPR 2017 Harm de Vries, Florian Strub, Sarath Chandar, Olivier Pietquin, Hugo Larochelle, Aaron Courville

Our key contribution is the collection of a large-scale dataset consisting of 150K human-played games with a total of 800K visual question-answer pairs on 66K images.

Object Discovery

A dataset and exploration of models for understanding video data through fill-in-the-blank question-answering

2 code implementations CVPR 2017 Tegan Maharaj, Nicolas Ballas, Anna Rohrbach, Aaron Courville, Christopher Pal

In addition to presenting statistics and a description of the dataset, we perform a detailed analysis of 5 different models' predictions, and compare these with human performance.

Language Modelling Object Detection +1

Professor Forcing: A New Algorithm for Training Recurrent Networks

1 code implementation NeurIPS 2016 Alex Lamb, Anirudh Goyal, Ying Zhang, Saizheng Zhang, Aaron Courville, Yoshua Bengio

We introduce the Professor Forcing algorithm, which uses adversarial domain adaptation to encourage the dynamics of the recurrent network to be the same when training the network and when sampling from the network over multiple time steps.

Domain Adaptation Handwriting generation +2

First Result on Arabic Neural Machine Translation

no code implementations8 Jun 2016 Amjad Almahairi, Kyunghyun Cho, Nizar Habash, Aaron Courville

Neural machine translation has become a major alternative to widely used phrase-based statistical machine translation.

Machine Translation Translation

Adversarially Learned Inference

7 code implementations2 Jun 2016 Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, Aaron Courville

We introduce the adversarially learned inference (ALI) model, which jointly learns a generation network and an inference network using an adversarial process.

Image-to-Image Translation

Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation

4 code implementations2 Jun 2016 Iulian Vlad Serban, Tim Klinger, Gerald Tesauro, Kartik Talamadupula, Bo-Wen Zhou, Yoshua Bengio, Aaron Courville

We introduce the multiresolution recurrent neural network, which extends the sequence-to-sequence framework to model natural language generation as two parallel discrete stochastic processes: a sequence of high-level coarse tokens, and a sequence of natural language tokens.

Dialogue Generation Response Generation

A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues

9 code implementations19 May 2016 Iulian Vlad Serban, Alessandro Sordoni, Ryan Lowe, Laurent Charlin, Joelle Pineau, Aaron Courville, Yoshua Bengio

Sequential data often possesses a hierarchical structure with complex dependencies between subsequences, such as found between the utterances in a dialogue.

Response Generation

Movie Description

no code implementations12 May 2016 Anna Rohrbach, Atousa Torabi, Marcus Rohrbach, Niket Tandon, Christopher Pal, Hugo Larochelle, Aaron Courville, Bernt Schiele

In addition we also collected and aligned movie scripts used in prior work and compare the two sources of descriptions.

Theano: A Python framework for fast computation of mathematical expressions

1 code implementation9 May 2016 The Theano Development Team, Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, Yoshua Bengio, Arnaud Bergeron, James Bergstra, Valentin Bisson, Josh Bleecher Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, Alexandre de Brébisson, Olivier Breuleux, Pierre-Luc Carrier, Kyunghyun Cho, Jan Chorowski, Paul Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Mélanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian Goodfellow, Matt Graham, Caglar Gulcehre, Philippe Hamel, Iban Harlouchet, Jean-Philippe Heng, Balázs Hidasi, Sina Honari, Arjun Jain, Sébastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, César Laurent, Sean Lee, Simon Lefrancois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert T. McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Pal, Razvan Pascanu, Mohammad Pezeshki, Colin Raffel, Daniel Renshaw, Matthew Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, John Salvatier, François Savard, Jan Schlüter, John Schulman, Gabriel Schwartz, Iulian Vlad Serban, Dmitriy Serdyuk, Samira Shabanian, Étienne Simon, Sigurd Spieckermann, S. Ramana Subramanyam, Jakub Sygnowski, Jérémie Tanguay, Gijs van Tulder, Joseph Turian, Sebastian Urban, Pascal Vincent, Francesco Visin, Harm de Vries, David Warde-Farley, Dustin J. Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, Ying Zhang

Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.

Dimensionality Reduction General Classification

Recurrent Batch Normalization

3 code implementations30 Mar 2016 Tim Cooijmans, Nicolas Ballas, César Laurent, Çağlar Gülçehre, Aaron Courville

We propose a reparameterization of LSTM that brings the benefits of batch normalization to recurrent neural networks.

General Classification Language Modelling +3

Discriminative Regularization for Generative Models

1 code implementation9 Feb 2016 Alex Lamb, Vincent Dumoulin, Aaron Courville

We propose to take advantage of this by using the representations from discriminative classifiers to augment the objective function corresponding to a generative model.

Dynamic Capacity Networks

1 code implementation24 Nov 2015 Amjad Almahairi, Nicolas Ballas, Tim Cooijmans, Yin Zheng, Hugo Larochelle, Aaron Courville

The low-capacity sub-networks are applied across most of the input, but also provide a guide to select a few portions of the input on which to apply the high-capacity sub-networks.

Variance Reduction in SGD by Distributed Importance Sampling

1 code implementation20 Nov 2015 Guillaume Alain, Alex Lamb, Chinnadhurai Sankar, Aaron Courville, Yoshua Bengio

This leads the model to update using an unbiased estimate of the gradient which also has minimum variance when the sampling proposal is proportional to the L2-norm of the gradient.

A Controller-Recognizer Framework: How necessary is recognition for control?

no code implementations19 Nov 2015 Marcin Moczulski, Kelvin Xu, Aaron Courville, Kyunghyun Cho

Recently there has been growing interest in building active visual object recognizers, as opposed to the usual passive recognizers which classifies a given static image into a predefined set of object categories.

Deconstructing the Ladder Network Architecture

no code implementations19 Nov 2015 Mohammad Pezeshki, Linxi Fan, Philemon Brakel, Aaron Courville, Yoshua Bengio

Although the empirical results are impressive, the Ladder Network has many components intertwined, whose contributions are not obvious in such a complex architecture.

Denoising

Delving Deeper into Convolutional Networks for Learning Video Representations

2 code implementations19 Nov 2015 Nicolas Ballas, Li Yao, Chris Pal, Aaron Courville

We propose an approach to learn spatio-temporal features in videos from intermediate visual representations we call "percepts" using Gated-Recurrent-Unit Recurrent Networks (GRUs). Our method relies on percepts that are extracted from all level of a deep convolutional network trained on the large ImageNet dataset.

Action Recognition Video Captioning

Task Loss Estimation for Sequence Prediction

1 code implementation19 Nov 2015 Dzmitry Bahdanau, Dmitriy Serdyuk, Philémon Brakel, Nan Rosemary Ke, Jan Chorowski, Aaron Courville, Yoshua Bengio

Our idea is that this score can be interpreted as an estimate of the task loss, and that the estimation error may be used as a consistent surrogate loss.

Speech Recognition

Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models

7 code implementations17 Jul 2015 Iulian V. Serban, Alessandro Sordoni, Yoshua Bengio, Aaron Courville, Joelle Pineau

We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models.

Word Embeddings

Describing Multimedia Content using Attention-based Encoder--Decoder Networks

no code implementations4 Jul 2015 Kyunghyun Cho, Aaron Courville, Yoshua Bengio

Whereas deep neural networks were first mostly used for classification tasks, they are rapidly expanding in the realm of structured output problems, where the observed target is composed of multiple random variables that have a rich joint distribution, given the input.

Machine Translation Speech Recognition +1

A Recurrent Latent Variable Model for Sequential Data

5 code implementations NeurIPS 2015 Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron Courville, Yoshua Bengio

In this paper, we explore the inclusion of latent random variables into the dynamic hidden state of a recurrent neural network (RNN) by combining elements of the variational autoencoder.

Describing Videos by Exploiting Temporal Structure

5 code implementations ICCV 2015 Li Yao, Atousa Torabi, Kyunghyun Cho, Nicolas Ballas, Christopher Pal, Hugo Larochelle, Aaron Courville

In this context, we propose an approach that successfully takes into account both the local and global temporal structure of videos to produce descriptions.

Action Recognition Video Description

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

76 code implementations10 Feb 2015 Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard Zemel, Yoshua Bengio

Inspired by recent work in machine translation and object detection, we introduce an attention based model that automatically learns to describe the content of images.

Image Captioning Translation

Generative Adversarial Nets

1 code implementation NeurIPS 2014 Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio

We propose a new framework for estimating generative models via adversarial nets, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake.

Deep Tempering

no code implementations1 Oct 2014 Guillaume Desjardins, Heng Luo, Aaron Courville, Yoshua Bengio

Restricted Boltzmann Machines (RBMs) are one of the fundamental building blocks of deep learning.

Generative Adversarial Networks

175 code implementations Proceedings of the 27th International Conference on Neural Information Processing Systems 2014 Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio

We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake.

Super-Resolution Time-Series Few-Shot Learning with Heterogeneous Channels

An empirical analysis of dropout in piecewise linear networks

no code implementations21 Dec 2013 David Warde-Farley, Ian J. Goodfellow, Aaron Courville, Yoshua Bengio

The recently introduced dropout training criterion for neural networks has been the subject of much attention due to its simplicity and remarkable effectiveness as a regularizer, as well as its interpretation as a training procedure for an exponentially large ensemble of networks that share parameters.

On the Challenges of Physical Implementations of RBMs

no code implementations18 Dec 2013 Vincent Dumoulin, Ian J. Goodfellow, Aaron Courville, Yoshua Bengio

Restricted Boltzmann machines (RBMs) are powerful machine learning models, but learning and some kinds of inference in the model require sampling-based approximations, which, in classical digital computers, are implemented using expensive MCMC.