Portrait de Chris Pal

Chris Pal

Membre académique principal
Chaire en IA Canada-CIFAR
Professeur titulaire, Polytechnique Montréal, Département de génie informatique et de génie logiciel
Professeur associé, Université de Montréal, Département d'informatique et de recherche opérationnelle
Sujets de recherche
Apprentissage profond

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

Stagiaire de recherche - McGill
Postdoctorat - HEC
Superviseur⋅e principal⋅e :
Collaborateur·rice de recherche - McGill
Superviseur⋅e principal⋅e :
Maîtrise recherche - UdeM
Doctorat - Polytechnique
Doctorat - McGill
Superviseur⋅e principal⋅e :
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Doctorat - Polytechnique
Maîtrise recherche - UdeM
Co-superviseur⋅e :
Collaborateur·rice alumni - Polytechnique
Doctorat - Polytechnique
Postdoctorat - McGill
Co-superviseur⋅e :
Maîtrise recherche - Polytechnique
Doctorat - UdeM
Co-superviseur⋅e :
Maîtrise recherche - Concordia
Co-superviseur⋅e :
Collaborateur·rice de recherche - UdeM
Maîtrise recherche - UdeM
Doctorat - UdeM
Doctorat - Polytechnique
Doctorat - Polytechnique
Doctorat - École de technologie suprérieure
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Postdoctorat - HEC
Superviseur⋅e principal⋅e :
Doctorat - Polytechnique
Superviseur⋅e principal⋅e :
Doctorat - McGill
Superviseur⋅e principal⋅e :
Doctorat - Polytechnique

Publications

Preface
Ismail Ben Ayed
Marleen de Bruijne
Maxime Descoteaux
Robust motion in-betweening
Félix Harvey
Mike Yurick
In this work we present a novel, robust transition generation technique that can serve as a new tool for 3D animators, based on adversarial … (voir plus)recurrent neural networks. The system synthesises high-quality motions that use temporally-sparse keyframes as animation constraints. This is reminiscent of the job of in-betweening in traditional animation pipelines, in which an animator draws motion frames between provided keyframes. We first show that a state-of-the-art motion prediction model cannot be easily converted into a robust transition generator when only adding conditioning information about future keyframes. To solve this problem, we then propose two novel additive embedding modifiers that are applied at each timestep to latent representations encoded inside the network's architecture. One modifier is a time-to-arrival embedding that allows variations of the transition length with a single model. The other is a scheduled target noise vector that allows the system to be robust to target distortions and to sample different transitions given fixed keyframes. To qualitatively evaluate our method, we present a custom MotionBuilder plugin that uses our trained model to perform in-betweening in production scenarios. To quantitatively evaluate performance on transitions and generalizations to longer time horizons, we present well-defined in-betweening benchmarks on a subset of the widely used Human3.6M dataset and on LaFAN1, a novel high quality motion capture dataset that is more appropriate for transition generation. We are releasing this new dataset along with this work, with accompanying code for reproducing our baseline results.
Towards an Unsupervised Method for Model Selection in Few-Shot Learning
Simon Guiroy
Vikas Verma
The study of generalization of neural networks in gradient-based meta-learning has recently great research interest. Previous work on the st… (voir plus)udy of the objective landscapes within the scope of few-shot classification empirically demonstrated that generalization to new tasks might be linked to the average inner product between their respective gradients vectors (Guiroy et al., 2019). Following that work, we study the effect that meta-training has on the learned space of representation of the network. Notably, we demonstrate that the global similarity in the space of representation, measured by the average inner product between the embeddings of meta-test examples, also correlates to generalization. Based on these observations, we propose a novel model-selection criterion for gradient-based meta-learning and experimentally validate its effectiveness.
Interactive Machine Comprehension with Information Seeking Agents
Xingdi Yuan
Jie Fu
Marc-Alexandre Côté
Yi Tay
Adam Trischler
Existing machine reading comprehension (MRC) models do not scale effectively to real-world applications like web-level information retrieval… (voir plus) and question answering (QA). We argue that this stems from the nature of MRC datasets: most of these are static environments wherein the supporting documents and all necessary information are fully observed. In this paper, we propose a simple method that reframes existing MRC datasets as interactive, partially observable environments. Specifically, we “occlude” the majority of a document’s text and add context-sensitive commands that reveal “glimpses” of the hidden text to a model. We repurpose SQuAD and NewsQA as an initial case study, and then show how the interactive corpora can be used to train a model that seeks relevant information through sequential decision making. We believe that this setting can contribute in scaling models to web-level QA scenarios.
Would you Rather? A New Benchmark for Learning Machine Alignment with Cultural Values and Social Preferences
Yi Tay
Donovan Ong
Jie Fu
Alvin Chan
Nancy Chen
Anh Tuan Luu
Medical Imaging with Deep Learning: MIDL 2020 - Short Paper Track
Ismail Ben Ayed
Marleen de Bruijne
Maxime Descoteaux
This compendium gathers all the accepted extended abstracts from the Third International Conference on Medical Imaging with Deep Learning (M… (voir plus)IDL 2020), held in Montreal, Canada, 6-9 July 2020. Note that only accepted extended abstracts are listed here, the Proceedings of the MIDL 2020 Full Paper Track are published in the Proceedings of Machine Learning Research (PMLR).
Active Domain Randomization
Bhairav Mehta
Manfred Diaz
Florian Golemo
Domain randomization is a popular technique for improving domain transfer, often used in a zero-shot setting when the target domain is unkno… (voir plus)wn or cannot easily be used for training. In this work, we empirically examine the effects of domain randomization on agent generalization. Our experiments show that domain randomization may lead to suboptimal, high-variance policies, which we attribute to the uniform sampling of environment parameters. We propose Active Domain Randomization, a novel algorithm that learns a parameter sampling strategy. Our method looks for the most informative environment variations within the given randomization ranges by leveraging the discrepancies of policy rollouts in randomized and reference environment instances. We find that training more frequently on these instances leads to better overall agent generalization. In addition, when domain randomization and policy transfer fail, Active Domain Randomization offers more insight into the deficiencies of both the chosen parameter ranges and the learned policy, allowing for more focused debugging. Our experiments across various physics-based simulated and a real-robot task show that this enhancement leads to more robust, consistent policies.
Leveraging cluster backbones for improving MAP inference in statistical relational models
Mohamed Hamza Ibrahim
Gilles Pesant
Role-Wise Data Augmentation for Knowledge Distillation
Jie Fu
Xue Geng
Zhijian Duan
Bohan Zhuang
Xingdi Yuan
Adam Trischler
Jie Lin
Vijay Chandrasekhar
Hao Dong
Revision in Continuous Space: Unsupervised Text Style Transfer without Adversarial Learning
Dayiheng Liu
Jie Fu
Yidan Zhang
Jiancheng Lv
Typical methods for unsupervised text style transfer often rely on two key ingredients: 1) seeking the explicit disentanglement of the conte… (voir plus)nt and the attributes, and 2) troublesome adversarial learning. In this paper, we show that neither of these components is indispensable. We propose a new framework that utilizes the gradients to revise the sentence in a continuous space during inference to achieve text style transfer. Our method consists of three key components: a variational auto-encoder (VAE), some attribute predictors (one for each attribute), and a content predictor. The VAE and the two types of predictors enable us to perform gradient-based optimization in the continuous space, which is mapped from sentences in a discrete space, to find the representation of a target sentence with the desired attributes and preserved content. Moreover, the proposed method naturally has the ability to simultaneously manipulate multiple fine-grained attributes, such as sentence length and the presence of specific words, when performing text style transfer tasks. Compared with previous adversarial learning based methods, the proposed method is more interpretable, controllable and easier to train. Extensive experimental studies on three popular text style transfer tasks show that the proposed method significantly outperforms five state-of-the-art methods.
Curriculum in Gradient-Based Meta-Reinforcement Learning
Bhairav Mehta
Tristan Deleu
Sharath Chandra Raparthy
Gradient-based meta-learners such as Model-Agnostic Meta-Learning (MAML) have shown strong few-shot performance in supervised and reinforcem… (voir plus)ent learning settings. However, specifically in the case of meta-reinforcement learning (meta-RL), we can show that gradient-based meta-learners are sensitive to task distributions. With the wrong curriculum, agents suffer the effects of meta-overfitting, shallow adaptation, and adaptation instability. In this work, we begin by highlighting intriguing failure cases of gradient-based meta-RL and show that task distributions can wildly affect algorithmic outputs, stability, and performance. To address this problem, we leverage insights from recent literature on domain randomization and propose meta Active Domain Randomization (meta-ADR), which learns a curriculum of tasks for gradient-based meta-RL in a similar as ADR does for sim2real transfer. We show that this approach induces more stable policies on a variety of simulated locomotion and navigation tasks. We assess in- and out-of-distribution generalization and find that the learned task distributions, even in an unstructured task space, greatly improve the adaptation performance of MAML. Finally, we motivate the need for better benchmarking in meta-RL that prioritizes \textit{generalization} over single-task adaption performance.
Exploring Structural Inductive Biases in Emergent Communication
Agnieszka M Slowik
Abhinav Gupta
William L. Hamilton
M. Jamnik
S. Holden
Human language and thought are characterized by the ability to systematically generate a potentially infinite number of complex structures (… (voir plus)e.g., sentences) from a finite set of familiar components (e.g., words). Recent works in emergent communication have discussed the propensity of artificial agents to develop a systematically compositional language through playing co-operative referential games. The degree of structure in the input data was found to affect the compositionality of the emerged communication protocols. Thus, we explore various structural priors in multi-agent communication and propose a novel graph referential game. We compare the effect of structural inductive bias (bag-of-words, sequences and graphs) on the emergence of compositional understanding of the input concepts measured by topographic similarity and generalization to unseen combinations of familiar properties. We empirically show that graph neural networks induce a better compositional language prior and a stronger generalization to out-of-domain data. We further perform ablation studies that show the robustness of the emerged protocol in graph referential games.