Christopher Pal

research

∙ 09/20/2023

Parallel-mentoring for Offline Model-based Optimization

We study offline model-based optimization to maximize a black-box object...

0 Can Chen, et al. ∙

research

∙ 06/15/2023

Block-State Transformer

State space models (SSMs) have shown impressive results on tasks that re...

0 Mahan Fathi, et al. ∙

research

∙ 06/07/2023

Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Design

In recent years, in-silico molecular design has received much attention ...

0 Julien Roy, et al. ∙

research

∙ 06/02/2023

Improving Generalization in Task-oriented Dialogues with Workflows and Action Plans

Task-oriented dialogue is difficult in part because it involves understa...

0 Stefania Raimondo, et al. ∙

research

∙ 05/25/2023

Are Diffusion Models Vision-And-Language Reasoners?

Text-conditioned image generation models have recently shown immense qua...

0 Benno Krojer, et al. ∙

research

∙ 04/07/2023

Conservative objective models are a special kind of contrastive divergence-based energy model

In this work we theoretically show that conservative objective models (C...

0 Christopher Beckham, et al. ∙

research

∙ 02/14/2023

Score-based Diffusion Models in Function Space

Diffusion models have recently emerged as a powerful framework for gener...

0 Jae Hyun Lim, et al. ∙

research

∙ 02/10/2023

Long-Context Language Decision Transformers and Exponential Tilt for Interactive Text Environments

Text-based game environments are challenging because agents must deal wi...

1 Nicolas Gontier, et al. ∙

research

∙ 11/26/2022

Receptive Field Refinement for Convolutional Neural Networks Reliably Improves Predictive Performance

Minimal changes to neural architectures (e.g. changing a single hyperpar...

0 Mats L. Richter, et al. ∙

research

∙ 11/19/2022

Towards good validation metrics for generative models in offline model-based optimisation

In this work we propose a principled evaluation framework for model-base...

0 Christopher Beckham, et al. ∙

research

∙ 11/02/2022

Attention-based Neural Cellular Automata

Recent extensions of Cellular Automata (CA) have incorporated key ideas ...

0 Mattie Tesfaldet, et al. ∙

research

∙ 10/21/2022

Bridging the Gap Between Target Networks and Functional Regularization

Bootstrapping is behind much of the successes of Deep Reinforcement Lear...

0 Alexandre Piché, et al. ∙

research

∙ 10/21/2022

Score-based Denoising Diffusion with Non-Isotropic Gaussian Noise Models

Generative models based on denoising diffusion techniques have led to an...

0 Vikram Voleti, et al. ∙

research

∙ 10/14/2022

Using Graph Algorithms to Pretrain Graph Completion Transformers

Recent work on Graph Neural Networks has demonstrated that self-supervis...

0 Jonathan Pilault, et al. ∙

research

∙ 08/16/2022

SMPL-IK: Learned Morphology-Aware Inverse Kinematics for AI Driven Artistic Workflows

Inverse Kinematics (IK) systems are often rigid with respect to their in...

8 Vikram Voleti, et al. ∙

research

∙ 08/03/2022

Improving Meta-Learning Generalization with Activation-Based Early-Stopping

Meta-Learning algorithms for few-shot learning aim to train neural netwo...

4 Simon Guiroy, et al. ∙

research

∙ 05/19/2022

Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation

Video prediction is a challenging task. The quality of video frames from...

23 Vikram Voleti, et al. ∙

research

∙ 03/30/2022

Challenges in leveraging GANs for few-shot data augmentation

In this paper, we explore the use of GAN-based few-shot data augmentatio...

11 Christopher Beckham, et al. ∙

research

∙ 01/05/2022

Does entity abstraction help generative Transformers reason?

Pre-trained language models (LMs) often struggle to reason logically or ...

14 Nicolas Gontier, et al. ∙

research

∙ 12/22/2021

Direct Behavior Specification via Constrained Reinforcement Learning

The standard formulation of Reinforcement Learning lacks a practical way...

0 Julien Roy, et al. ∙

research

∙ 12/14/2021

Learning to Guide and to Be Guided in the Architect-Builder Problem

We are interested in interactive agents that learn to coordinate, namely...

0 Paul Barde, et al. ∙

research

∙ 10/28/2021

From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence

Machine learning has long since become a keystone technology, accelerati...

46 Nicholas Roy, et al. ∙

research

∙ 09/07/2021

Simple Video Generation using Neural ODEs

Despite having been studied to a great extent, the task of conditional g...

0 David Kanaa, et al. ∙

research

∙ 07/02/2021

Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning

Inducing causal relationships from observations is a classic problem in ...

7 Nan Rosemary Ke, et al. ∙

research

∙ 06/15/2021

Multi-Resolution Continuous Normalizing Flows

Recent work has shown that Neural Ordinary Differential Equations (ODEs)...

0 Vikram Voleti, et al. ∙

research

∙ 06/04/2021

Beyond Target Networks: Improving Deep Q-learning with Functional Regularization

Target networks are at the core of recent success in Reinforcement Learn...

0 Alexandre Piché, et al. ∙

research

∙ 02/19/2021

Latent Variable Nested Set Transformers AutoBots

Humans have the innate ability to attend to the most relevant actors in ...

0 Roger Girgis, et al. ∙

research

∙ 02/09/2021

Robust Motion In-betweening

In this work we present a novel, robust transition generation technique ...

12 Félix G. Harvey, et al. ∙

research

∙ 10/21/2020

Learning to Summarize Long Texts with Memory Compression and Transfer

We introduce Mem2Mem, a memory-to-memory mechanism for hierarchical recu...

0 Jaehong Park, et al. ∙

research

∙ 10/06/2020

Reinforcement Learning with Random Delays

Action and observation delays commonly occur in many Reinforcement Learn...

0 Simon Ramstedt, et al. ∙

research

∙ 09/30/2020

Measuring Systematic Generalization in Neural Proof Generation with Transformers

We are interested in understanding how well Transformer language models ...

6 Nicolas Gontier, et al. ∙

research

∙ 09/19/2020

Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters Less Data

Multi-Task Learning (MTL) has emerged as a promising approach for transf...

7 Jonathan Pilault, et al. ∙

research

∙ 08/21/2020

Action-Based Representation Learning for Autonomous Driving

Human drivers produce a vast amount of data which could, in principle, b...

9 Yi Xiao, et al. ∙

research

∙ 06/23/2020

Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization

Adversarial imitation learning alternates between learning a discriminat...

15 Paul Barde, 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

∙ 02/21/2020

On the impressive performance of randomly weighted encoders in summarization tasks

In this work, we investigate the performance of untrained randomly initi...

0 Jonathan Pilault, et al. ∙

research

∙ 11/11/2019

Real-Time Reinforcement Learning

Markov Decision Processes (MDPs), the mathematical framework underlying ...

45 Simon Ramstedt, et al. ∙

research

∙ 10/06/2019

Neural Multisensory Scene Inference

For embodied agents to infer representations of the underlying 3D physic...

7 Jae Hyun Lim, et al. ∙

research

∙ 09/07/2019

On Extractive and Abstractive Neural Document Summarization with Transformer Language Models

We present a method to produce abstractive summaries of long documents t...

0 Sandeep Subramanian, et al. ∙

research

∙ 08/28/2019

Interactive Language Learning by Question Answering

Humans observe and interact with the world to acquire knowledge. However...

0 Xingdi Yuan, et al. ∙

research

∙ 08/27/2019

Interactive Machine Comprehension with Information Seeking Agents

Existing machine reading comprehension (MRC) models do not scale effecti...

5 Xingdi Yuan, et al. ∙

research

∙ 08/06/2019

Promoting Coordination through Policy Regularization in Multi-Agent Reinforcement Learning

A central challenge in multi-agent reinforcement learning is the inducti...

0 Paul Barde, et al. ∙

research

∙ 07/16/2019

Towards Understanding Generalization in Gradient-Based Meta-Learning

In this work we study generalization of neural networks in gradient-base...

9 Simon Guiroy, et al. ∙

research

∙ 06/27/2019

Supervise Thyself: Examining Self-Supervised Representations in Interactive Environments

Self-supervised methods, wherein an agent learns representations solely ...

4 Evan Racah, et al. ∙

research

∙ 06/04/2019

Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study

Neural generative models have been become increasingly popular when buil...

2 Chinnadhurai Sankar, et al. ∙

research

∙ 03/07/2019

Adversarial Mixup Resynthesizers

In this paper, we explore new approaches to combining information encode...

12 Christopher Beckham, et al. ∙

research

∙ 01/30/2019

A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms

We propose to meta-learn causal structures based on how fast a learner a...

0 Yoshua Bengio, et al. ∙

research

∙ 01/04/2019

Dataflow-based Joint Quantization of Weights and Activations for Deep Neural Networks

This paper addresses a challenging problem - how to reduce energy consum...

14 Xue Geng, et al. ∙

research

∙ 10/04/2018

Recurrent Transition Networks for Character Locomotion

Manually authoring transition animations for a complete locomotion syste...

16 Félix G. Harvey, et al. ∙

research

∙ 03/25/2018

Unsupervised Depth Estimation, 3D Face Rotation and Replacement

We present an unsupervised approach for learning to estimate three dimen...

0 Joel Ruben Antony Moniz, et al. ∙

Christopher Pal

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