Chris Pal

Biographie

Christopher Pal est titulaire d'une chaire en IA Canada-CIFAR, professeur titulaire à Polytechnique Montréal et professeur adjoint au Département d'informatique et de recherche opérationnelle (DIRO) de l'Université de Montréal. Il est également chercheur émérite à ServiceNow Research. Il est engagé dans la recherche sur l'intelligence artificielle et l'apprentissage automatique depuis plus de 25 ans, publiant souvent des travaux sur les méthodes de modélisation du langage à grande échelle et les techniques de modélisation générative. Il a obtenu un doctorat en informatique à l'Université de Waterloo.

Étudiants actuels

Mai Ababneh

Stagiaire de recherche - McGill

ababneh.mai@gmail.com

Shubham Agarwal

Postdoctorat - HEC

Superviseur⋅e principal⋅e :

Paul Barde

Collaborateur·rice de recherche - McGill

Superviseur⋅e principal⋅e :

paul.b.barde@gmail.com

Maîtrise recherche - UdeM

Chris Beckham

Doctorat - Polytechnique

Can (Sam) Chen

Doctorat - McGill

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Doctorat - Polytechnique

Chris Emezue

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Collaborateur·rice alumni - Polytechnique

Roger Girgis

Doctorat - Polytechnique

Florian Golemo

Postdoctorat - McGill

Co-superviseur⋅e :

Maîtrise recherche - Polytechnique

Doctorat - UdeM

Co-superviseur⋅e :

Yousef Kotp

Maîtrise recherche - Concordia

Co-superviseur⋅e :

Collaborateur·rice de recherche - UdeM

Maîtrise recherche - UdeM

Olga Luo

Doctorat - UdeM

Joel Moniz

Doctorat - Polytechnique

Jonathan Pilault

Doctorat - Polytechnique

Juan Rodriguez

Doctorat - École de technologie suprérieure

Luke Rowe

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Gaurav Sahu

Postdoctorat - HEC

Superviseur⋅e principal⋅e :

Doctorat - Polytechnique

Superviseur⋅e principal⋅e :

Doctorat - McGill

Superviseur⋅e principal⋅e :

Doctorat - Polytechnique

Spécification directe du comportement par apprentissage par renforcement sous contrainte

Billets de blogue

Direct Behavior Specification via Constrained Reinforcement Learning

31 août 2022

par

Julien Roy

Roger Girgis

Joshua Romoff

Pierre-Luc Bacon

Chris Pal

Lire l'article

Publications

Attention-based Neural Cellular Automata

Mattie Tesfaldet

Recent extensions of Cellular Automata (CA) have incorporated key ideas from modern deep learning, dramatically extending their capabilities… (voir plus) and catalyzing a new family of Neural Cellular Automata (NCA) techniques. Inspired by Transformer-based architectures, our work presents a new class of _attention-based_ NCAs formed using a spatially localized—yet globally organized—self-attention scheme. We introduce an instance of this class named _Vision Transformer Cellular Automata (ViTCA)_. We present quantitative and qualitative results on denoising autoencoding across six benchmark datasets, comparing ViTCA to a U-Net, a U-Net-based CA baseline (UNetCA), and a Vision Transformer (ViT). When comparing across architectures configured to similar parameter complexity, ViTCA architectures yield superior performance across all benchmarks and for nearly every evaluation metric. We present an ablation study on various architectural configurations of ViTCA, an analysis of its effect on cell states, and an investigation on its inductive biases. Finally, we examine its learned representations via linear probes on its converged cell state hidden representations, yielding, on average, superior results when compared to our U-Net, ViT, and UNetCA baselines.

Challenges in leveraging GANs for few-shot data augmentation

Christopher Beckham

Issam Hadj Laradji

Pau Rodriguez

David Vazquez

2022-01-01

arXiv.org (prépublication)

doi.org

Latent Variable Sequential Set Transformers for Joint Multi-Agent Motion Prediction

Roger Girgis

Florian Golemo

Felipe Codevilla

Martin Weiss

Jim Aldon D'Souza

Samira Ebrahimi Kahou

Felix Heide

Robust multi-agent trajectory prediction is essential for the safe control of robotic systems. A major challenge is to efficiently learn a r… (voir plus)epresentation that approximates the true joint distribution of contextual, social, and temporal information to enable planning. We propose Latent Variable Sequential Set Transformers which are encoder-decoder architectures that generate scene-consistent multi-agent trajectories. We refer to these architectures as “AutoBots”. The encoder is a stack of interleaved temporal and social multi-head self-attention (MHSA) modules which alternately perform equivariant processing across the temporal and social dimensions. The decoder employs learnable seed parameters in combination with temporal and social MHSA modules allowing it to perform inference over the entire future scene in a single forward pass efficiently. AutoBots can produce either the trajectory of one ego-agent or a distribution over the future trajectories for all agents in the scene. For the single-agent prediction case, our model achieves top results on the global nuScenes vehicle motion prediction leaderboard, and produces strong results on the Argoverse vehicle prediction challenge. In the multi-agent setting, we evaluate on the synthetic partition of TrajNet++ dataset to showcase the model’s socially-consistent predictions. We also demonstrate our model on general sequences of sets and provide illustrative experiments modelling the sequential structure of the multiple strokes that make up symbols in the Omniglot data. A distinguishing feature of AutoBots is that all models are trainable on a single desktop GPU (1080 Ti) in under 48h.

2022-01-01

ICLR (publié)

MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation

Vikram Voleti

Alexia Jolicoeur-Martineau

Video prediction is a challenging task. The quality of video frames from current state-of-the-art (SOTA) generative models tends to be poor … (voir plus)and generalization beyond the training data is difficult. Furthermore, existing prediction frameworks are typically not capable of simultaneously handling other video-related tasks such as unconditional generation or interpolation. In this work, we devise a general-purpose framework called Masked Conditional Video Diffusion (MCVD) for all of these video synthesis tasks using a probabilistic conditional score-based denoising diffusion model, conditioned on past and/or future frames. We train the model in a manner where we randomly and independently mask all the past frames or all the future frames. This novel but straightforward setup allows us to train a single model that is capable of executing a broad range of video tasks, specifically: future/past prediction -- when only future/past frames are masked; unconditional generation -- when both past and future frames are masked; and interpolation -- when neither past nor future frames are masked. Our experiments show that this approach can generate high-quality frames for diverse types of videos. Our MCVD models are built from simple non-recurrent 2D-convolutional architectures, conditioning on blocks of frames and generating blocks of frames. We generate videos of arbitrary lengths autoregressively in a block-wise manner. Our approach yields SOTA results across standard video prediction and interpolation benchmarks, with computation times for training models measured in 1-12 days using

Neural Attentive Circuits

Nasim Rahaman

Martin Weiss

Francesco Locatello

Yoshua Bengio

Bernhard Schölkopf

Li Erran Li

Nicolas Ballas

Recent work has seen the development of general purpose neural architectures that can be trained to perform tasks across diverse data modali… (voir plus)ties. General purpose models typically make few assumptions about the underlying data-structure and are known to perform well in the large-data regime. At the same time, there has been growing interest in modular neural architectures that represent the data using sparsely interacting modules. These models can be more robust out-of-distribution, computationally efficient, and capable of sample-efficient adaptation to new data. However, they tend to make domain-specific assumptions about the data, and present challenges in how module behavior (i.e., parameterization) and connectivity (i.e., their layout) can be jointly learned. In this work, we introduce a general purpose, yet modular neural architecture called Neural Attentive Circuits (NACs) that jointly learns the parameterization and a sparse connectivity of neural modules without using domain knowledge. NACs are best understood as the combination of two systems that are jointly trained end-to-end: one that determines the module configuration and the other that executes it on an input. We demonstrate qualitatively that NACs learn diverse and meaningful module configurations on the NLVR2 dataset without additional supervision. Quantitatively, we show that by incorporating modularity in this way, NACs improve upon a strong non-modular baseline in terms of low-shot adaptation on CIFAR and CUBs dataset by about 10%, and OOD robustness on Tiny ImageNet-R by about 2.5%. Further, we find that NACs can achieve an 8x speedup at inference time while losing less than 3% performance. Finally, we find NACs to yield competitive results on diverse data modalities spanning point-cloud classification, symbolic processing and text-classification from ASCII bytes, thereby confirming its general purpose nature.

Overcoming challenges in leveraging GANs for few-shot data augmentation

Christopher Beckham

Issam Hadj Laradji

Pau Rodriguez

David Vazquez

2022-01-01

CoLLAs (publié)

proceedings.mlr.press

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

Christopher Beckham

Alexandre Piché

David Vazquez

In this work we propose a principled evaluation framework for model-based optimisation to measure how well a generative model can extrapolat… (voir plus)e. We achieve this by interpreting the training and validation splits as draws from their respective ‘truncated’ ground truth distributions, where examples in the validation set contain scores much larger than those in the training set. Model selection is performed on the validation set for some prescribed validation metric. A major research question however is in determining what validation metric correlates best with the expected value of generated candidates with respect to the ground truth oracle; work towards answering this question can translate to large economic gains since it is expensive to evaluate the ground truth oracle in the real world. We compare various validation metrics for generative adversarial networks using our framework. We also discuss limitations with our framework with respect to existing datasets and how progress can be made to mitigate them. 1

2022-01-01

arXiv.org (prépublication)

doi.org

Learned Image Compression for Machine Perception

Felipe Codevilla

Jean Gabriel Simard

Ross Goroshin

2021-11-03

ArXiv (prépublication)

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

Nicholas Roy

Ingmar Posner

T. Barfoot

Philippe Beaudoin

Yoshua Bengio

Jeannette Bohg

Oliver Brock

Isabelle Depatie

Dieter Fox

D. Koditschek

Tom'as Lozano-p'erez

Vikash K. Mansinghka

Blake Richards

Dorsa Sadigh

Stefan Schaal

G. Sukhatme

Denis Therien

Marc Emile Toussaint

Michiel van de Panne

2021-10-28

ArXiv (prépublication)

Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning

Nan Rosemary Ke

Aniket Rajiv Didolkar

Sarthak Mittal

Anirudh Goyal

Guillaume Lajoie

Stefan Bauer

Danilo Jimenez Rezende

Yoshua Bengio

Michael Curtis Mozer

Inducing causal relationships from observations is a classic problem in machine learning. Most work in causality starts from the premise tha… (voir plus)t the causal variables themselves are observed. However, for AI agents such as robots trying to make sense of their environment, the only observables are low-level variables like pixels in images. To generalize well, an agent must induce high-level variables, particularly those which are causal or are affected by causal variables. A central goal for AI and causality is thus the joint discovery of abstract representations and causal structure. However, we note that existing environments for studying causal induction are poorly suited for this objective because they have complicated task-specific causal graphs which are impossible to manipulate parametrically (e.g., number of nodes, sparsity, causal chain length, etc.). In this work, our goal is to facilitate research in learning representations of high-level variables as well as causal structures among them. In order to systematically probe the ability of methods to identify these variables and structures, we design a suite of benchmarking RL environments. We evaluate various representation learning algorithms from the literature and find that explicitly incorporating structure and modularity in models can help causal induction in model-based reinforcement learning.

2021-10-11

NeurIPS.cc/2021/Track/Datasets_and_Benchmarks/Round2 (publié)

Action-Based Representation Learning for Autonomous Driving

Yi Xiao

Felipe Codevilla

Antonio M. López

Human drivers produce a vast amount of data which could, in principle, be used to improve autonomous driving systems. Unfortunately, seeming… (voir plus)ly straightforward approaches for creating end-to-end driving models that map sensor data directly into driving actions are problematic in terms of interpretability, and typically have significant difficulty dealing with spurious correlations. Alternatively, we propose to use this kind of action-based driving data for learning representations. Our experiments show that an affordance-based driving model pre-trained with this approach can leverage a relatively small amount of weakly annotated imagery and outperform pure end-to-end driving models, while being more interpretable. Further, we demonstrate how this strategy outperforms previous methods based on learning inverse dynamics models as well as other methods based on heavy human supervision (ImageNet).

2021-10-04

Proceedings of the 2020 Conference on Robot Learning (publié)

proceedings.mlr.press

Simple Video Generation using Neural ODEs

David Kanaa

Vikram Voleti

Samira Ebrahimi Kahou

Despite having been studied to a great extent, the task of conditional generation of sequences of frames, or videos, remains extremely chall… (voir plus)enging. It is a common belief that a key step towards solving this task resides in modelling accurately both spatial and temporal information in video signals. A promising direction to do so has been to learn latent variable models that predict the future in latent space and project back to pixels, as suggested in recent literature. Following this line of work and building on top of a family of models introduced in prior work, Neural ODE, we investigate an approach that models time-continuous dynamics over a continuous latent space with a differential equation with respect to time. The intuition behind this approach is that these trajectories in latent space could then be extrapolated to generate video frames beyond the time steps for which the model is trained. We show that our approach yields promising results in the task of future frame prediction on the Moving MNIST dataset with 1 and 2 digits.

2021-09-07

ArXiv (prépublication)