Aaron Courville

research

∙ 07/17/2023

Meta-Value Learning: a General Framework for Learning with Learning Awareness

Gradient-based learning in multi-agent systems is difficult because the ...

0 Tim Cooijmans, et al. ∙

research

∙ 05/30/2023

Bigger, Better, Faster: Human-level Atari with human-level efficiency

We introduce a value-based RL agent, which we call BBF, that achieves su...

0 Max Schwarzer, et al. ∙

research

∙ 05/26/2023

Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets

Combinatorial optimization (CO) problems are often NP-hard and thus out ...

0 Dinghuai Zhang, et al. ∙

research

∙ 02/11/2023

Distributional GFlowNets with Quantile Flows

Generative Flow Networks (GFlowNets) are a new family of probabilistic s...

0 Dinghuai Zhang, et al. ∙

research

∙ 02/01/2023

Versatile Energy-Based Models for High Energy Physics

Energy-based models have the natural advantage of flexibility in the for...

0 Taoli Cheng, et al. ∙

research

∙ 11/15/2022

On the Compositional Generalization Gap of In-Context Learning

Pretrained large generative language models have shown great performance...

0 Arian Hosseini, et al. ∙

research

∙ 11/15/2022

Teaching Algorithmic Reasoning via In-context Learning

Large language models (LLMs) have shown increasing in-context learning c...

0 Hattie Zhou, et al. ∙

research

∙ 10/07/2022

Generative Augmented Flow Networks

The Generative Flow Network is a probabilistic framework where an agent ...

0 Ling Pan, et al. ∙

research

∙ 10/03/2022

Latent State Marginalization as a Low-cost Approach for Improving Exploration

While the maximum entropy (MaxEnt) reinforcement learning (RL) framework...

0 Dinghuai Zhang, et al. ∙

research

∙ 09/24/2022

Unsupervised Model-based Pre-training for Data-efficient Control from Pixels

Controlling artificial agents from visual sensory data is an arduous tas...

0 Sai Rajeswar, et al. ∙

research

∙ 08/16/2022

Riemannian Diffusion Models

Diffusion models are recent state-of-the-art methods for image generatio...

16 Chin-Wei Huang, et al. ∙

research

∙ 06/30/2022

R-MelNet: Reduced Mel-Spectral Modeling for Neural TTS

This paper introduces R-MelNet, a two-part autoregressive architecture w...

0 Kyle Kastner, et al. ∙

research

∙ 06/03/2022

Beyond Tabula Rasa: Reincarnating Reinforcement Learning

Learning tabula rasa, that is without any prior knowledge, is the preval...

0 Rishabh Agarwal, et al. ∙

research

∙ 06/02/2022

Expressiveness and Learnability: A Unifying View for Evaluating Self-Supervised Learning

We propose a unifying view to analyze the representation quality of self...

0 Yuchen Lu, et al. ∙

research

∙ 06/01/2022

Cascaded Video Generation for Videos In-the-Wild

Videos can be created by first outlining a global view of the scene and ...

0 Lluis Castrejon, et al. ∙

research

∙ 05/16/2022

The Primacy Bias in Deep Reinforcement Learning

This work identifies a common flaw of deep reinforcement learning (RL) a...

0 Evgenii Nikishin, et al. ∙

research

∙ 04/01/2022

Simplicial Embeddings in Self-Supervised Learning and Downstream Classification

We introduce Simplicial Embeddings (SEMs) as a way to constrain the enco...

0 Samuel Lavoie, et al. ∙

research

∙ 02/03/2022

Generative Flow Networks for Discrete Probabilistic Modeling

We present energy-based generative flow networks (EB-GFN), a novel proba...

26 Dinghuai Zhang, et al. ∙

research

∙ 02/01/2022

Fortuitous Forgetting in Connectionist Networks

Forgetting is often seen as an unwanted characteristic in both human and...

0 Hattie Zhou, et al. ∙

research

∙ 01/18/2022

Invariant Representation Driven Neural Classifier for Anti-QCD Jet Tagging

We leverage representation learning and the inductive bias in neural-net...

0 Taoli Cheng, et al. ∙

research

∙ 12/17/2021

MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling

Musical expression requires control of both what notes are played, and h...

6 Yusong Wu, et al. ∙

research

∙ 12/09/2021

DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization

Despite overparameterization, deep networks trained via supervised learn...

0 Aviral Kumar, et al. ∙

research

∙ 11/23/2021

Multi-label Iterated Learning for Image Classification with Label Ambiguity

Transfer learning from large-scale pre-trained models has become essenti...

5 Sai Rajeswar, et al. ∙

research

∙ 10/06/2021

Unifying Likelihood-free Inference with Black-box Sequence Design and Beyond

Black-box optimization formulations for biological sequence design have ...

5 Dinghuai Zhang, et al. ∙

research

∙ 09/22/2021

On Bonus-Based Exploration Methods in the Arcade Learning Environment

Research on exploration in reinforcement learning, as applied to Atari 2...

0 Adrien Ali Taïga, et al. ∙

research

∙ 08/30/2021

Deep Reinforcement Learning at the Edge of the Statistical Precipice

Deep reinforcement learning (RL) algorithms are predominantly evaluated ...

0 Rishabh Agarwal, et al. ∙

research

∙ 06/09/2021

Pretraining Representations for Data-Efficient Reinforcement Learning

Data efficiency is a key challenge for deep reinforcement learning. We a...

0 Max Schwarzer, et al. ∙

research

∙ 06/05/2021

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

Can models with particular structure avoid being biased towards spurious...

0 Dinghuai Zhang, et al. ∙

research

∙ 06/05/2021

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

Discrete-time diffusion-based generative models and score matching metho...

0 Chin-Wei Huang, et al. ∙

research

∙ 06/04/2021

Hierarchical Video Generation for Complex Data

Videos can often be created by first outlining a global description of t...

0 Lluis Castrejon, et al. ∙

research

∙ 05/07/2021

Understanding by Understanding Not: Modeling Negation in Language Models

Negation is a core construction in natural language. Despite being very ...

0 Arian Hosseini, et al. ∙

research

∙ 05/03/2021

Iterated learning for emergent systematicity in VQA

Although neural module networks have an architectural bias towards compo...

0 Ankit Vani, et al. ∙

research

∙ 04/01/2021

Touch-based Curiosity for Sparse-Reward Tasks

Robots in many real-world settings have access to force/torque sensors i...

12 Sai Rajeswar, et al. ∙

research

∙ 03/19/2021

Learning Task Decomposition with Ordered Memory Policy Network

Many complex real-world tasks are composed of several levels of sub-task...

35 Yuchen Lu, et al. ∙

research

∙ 03/04/2021

Continuous Coordination As a Realistic Scenario for Lifelong Learning

Current deep reinforcement learning (RL) algorithms are still highly tas...

0 Hadi Nekoei, et al. ∙

research

∙ 01/25/2021

Emergent Communication under Competition

The literature in modern machine learning has only negative results for ...

0 Michael Noukhovitch, et al. ∙

research

∙ 12/10/2020

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

Flow-based models are powerful tools for designing probabilistic models ...

0 Chin-Wei Huang, et al. ∙

research

∙ 12/01/2020

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

There are two major classes of natural language grammars – the dependenc...

2 Yikang Shen, et al. ∙

research

∙ 11/18/2020

Gradient Starvation: A Learning Proclivity in Neural Networks

We identify and formalize a fundamental gradient descent phenomenon resu...

1 Mohammad Pezeshki, et al. ∙

research

∙ 11/11/2020

Unsupervised Learning of Dense Visual Representations

Contrastive self-supervised learning has emerged as a promising approach...

0 Pedro O. Pinheiro, et al. ∙

research

∙ 10/22/2020

NU-GAN: High resolution neural upsampling with GAN

In this paper, we propose NU-GAN, a new method for resampling audio from...

0 Rithesh Kumar, et al. ∙

research

∙ 10/20/2020

Neural Approximate Sufficient Statistics for Implicit Models

We consider the fundamental problem of how to automatically construct su...

0 Yanzhi Chen, et al. ∙

research

∙ 10/09/2020

Recursive Top-Down Production for Sentence Generation with Latent Trees

We model the recursive production property of context-free grammars for ...

0 Shawn Tan, et al. ∙

research

∙ 10/06/2020

Supervised Seeded Iterated Learning for Interactive Language Learning

Language drift has been one of the major obstacles to train language mod...

0 Yuchen Lu, et al. ∙

research

∙ 10/03/2020

Integrating Categorical Semantics into Unsupervised Domain Translation

While unsupervised domain translation (UDT) has seen a lot of success re...

0 Samuel Lavoie-Marchildon, et al. ∙

research

∙ 07/12/2020

Data-Efficient Reinforcement Learning with Momentum Predictive Representations

While deep reinforcement learning excels at solving tasks where large am...

0 Max Schwarzer, et al. ∙

research

∙ 07/11/2020

Generative Graph Perturbations for Scene Graph Prediction

Inferring objects and their relationships from an image is useful in man...

0 Boris Knyazev, et al. ∙

research

∙ 06/09/2020

AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation

Entropy is ubiquitous in machine learning, but it is in general intracta...

12 Jae Hyun Lim, et al. ∙

research

∙ 05/17/2020

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

Scene graph generation (SGG) aims to predict graph-structured descriptio...

0 Boris Knyazev, et al. ∙

research

∙ 05/06/2020

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

We present Korbit, a large-scale, open-domain, mixed-interface, dialogue...

1 Iulian Vlad Serban, et al. ∙

Aaron Courville

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