Chris Pal

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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

Paul Barde

Collaborateur·rice de recherche - McGill

Superviseur⋅e principal⋅e :

Maîtrise recherche - UdeM

Can (Sam) Chen

Collaborateur·rice alumni - McGill

Superviseur⋅e principal⋅e :

Xue (Steve) Liu

Léa Demeule

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Doctorat - Polytechnique

Chris Emezue

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - Polytechnique

Simon Guiroy

Doctorat - UdeM

Co-superviseur⋅e :

Yousef Kotp

Maîtrise recherche - Concordia

Co-superviseur⋅e :

Doctorat - Polytechnique

Co-superviseur⋅e :

Maîtrise recherche - UdeM

Google Scholar

Olga Luo

Doctorat - UdeM

Doctorat - UdeM

Joel Moniz

Doctorat - Polytechnique

Juan Rodriguez

Doctorat - École de technologie suprérieure

Google Scholar

Luke Rowe

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Gaurav Sahu

Postdoctorat - HEC

Superviseur⋅e principal⋅e :

Doctorat - Polytechnique

Superviseur⋅e principal⋅e :

Collaborateur·rice de recherche - McGill

Superviseur⋅e principal⋅e :

Postdoctorat - Polytechnique

Co-superviseur⋅e :

Doctorat - UdeM

Collaborateur·rice de recherche

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

Focused Hierarchical RNNs for Conditional Sequence Processing

Nan Rosemary Ke

Adam Trischler

Recurrent Neural Networks (RNNs) with attention mechanisms have obtained state-of-the-art results for many sequence processing tasks. Most o… (voir plus)f these models use a simple form of encoder with attention that looks over the entire sequence and assigns a weight to each token independently. We present a mechanism for focusing RNN encoders for sequence modelling tasks which allows them to attend to key parts of the input as needed. We formulate this using a multi-layer conditional sequence encoder that reads in one token at a time and makes a discrete decision on whether the token is relevant to the context or question being asked. The discrete gating mechanism takes in the context embedding and the current hidden state as inputs and controls information flow into the layer above. We train it using policy gradient methods. We evaluate this method on several types of tasks with different attributes. First, we evaluate the method on synthetic tasks which allow us to evaluate the model for its generalization ability and probe the behavior of the gates in more controlled settings. We then evaluate this approach on large scale Question Answering tasks including the challenging MS MARCO and SearchQA tasks. Our models shows consistent improvements for both tasks over prior work and our baselines. It has also shown to generalize significantly better on synthetic tasks as compared to the baselines.

2018-07-02

Proceedings of the 35th International Conference on Machine Learning (publié)

proceedings.mlr.press

Sparse Attentive Backtracking: Temporal Credit Assignment Through Reminding

Nan Rosemary Ke

Michael C. Mozer

Learning long-term dependencies in extended temporal sequences requires credit assignment to events far back in the past. The most common me… (voir plus)thod for training recurrent neural networks, back-propagation through time (BPTT), requires credit information to be propagated backwards through every single step of the forward computation, potentially over thousands or millions of time steps. This becomes computationally expensive or even infeasible when used with long sequences. Importantly, biological brains are unlikely to perform such detailed reverse replay over very long sequences of internal states (consider days, months, or years.) However, humans are often reminded of past memories or mental states which are associated with the current mental state. We consider the hypothesis that such memory associations between past and present could be used for credit assignment through arbitrarily long sequences, propagating the credit assigned to the current state to the associated past state. Based on this principle, we study a novel algorithm which only back-propagates through a few of these temporal skip connections, realized by a learned attention mechanism that associates current states with relevant past states. We demonstrate in experiments that our method matches or outperforms regular BPTT and truncated BPTT in tasks involving particularly long-term dependencies, but without requiring the biologically implausible backward replay through the whole history of states. Additionally, we demonstrate that the proposed method transfers to longer sequences significantly better than LSTMs trained with BPTT and LSTMs trained with full self-attention.

2017-12-31

Advances in Neural Information Processing Systems (publié)

Towards Deep Conversational Recommendations

Raymond Li

Samira Ebrahimi Kahou

Hannes Schulz

Vincent Michalski

Laurent Charlin

Chris Pal

There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational recommendat… (voir plus)ion is an interesting setting for the scientific exploration of dialogue with natural language as the associated discourse involves goal-driven dialogue that often transforms naturally into more free-form chat. This paper provides two contributions. First, until now there has been no publicly available large-scale dataset consisting of real-world dialogues centered around recommendations. To address this issue and to facilitate our exploration here, we have collected ReDial, a dataset consisting of over 10,000 conversations centered around the theme of providing movie recommendations. We make this data available to the community for further research. Second, we use this dataset to explore multiple facets of conversational recommendations. In particular we explore new neural architectures, mechanisms, and methods suitable for composing conversational recommendation systems. Our dataset allows us to systematically probe model sub-components addressing different parts of the overall problem domain ranging from: sentiment analysis and cold-start recommendation generation to detailed aspects of how natural language is used in this setting in the real world. We combine such sub-components into a full-blown dialogue system and examine its behavior.

2017-12-31

Advances in Neural Information Processing Systems 31 (NeurIPS 2018) (publié)

Towards Text Generation with Adversarially Learned Neural Outlines.

Sandeep Subramanian

Sai Rajeswar

Alessandro Sordoni

Adam Trischler

Aaron C. Courville

Chris Pal

2017-12-31

Advances in Neural Information Processing Systems 31 (NeurIPS 2018) (publié)

dblp.uni-trier.de

Learning Normalized Inputs for Iterative Estimation in Medical Image Segmentation

Michal Drozdzal

Gabriel Chartrand

Eugene Vorontsov

Lisa Di Jorio

An Tang

Adriana Romero

Yoshua Bengio

Chris Pal

Samuel Kadoury

In this paper, we introduce a simple, yet powerful pipeline for medical image segmentation that combines Fully Convolutional Networks (FCNs)… (voir plus) with Fully Convolutional Residual Networks (FC-ResNets). We propose and examine a design that takes particular advantage of recent advances in the understanding of both Convolutional Neural Networks as well as ResNets. Our approach focuses upon the importance of a trainable pre-processing when using FC-ResNets and we show that a low-capacity FCN model can serve as a pre-processor to normalize medical input data. In our image segmentation pipeline, we use FCNs to obtain normalized images, which are then iteratively refined by means of a FC-ResNet to generate a segmentation prediction. As in other fully convolutional approaches, our pipeline can be used off-the-shelf on different image modalities. We show that using this pipeline, we exhibit state-of-the-art performance on the challenging Electron Microscopy benchmark, when compared to other 2D methods. We improve segmentation results on CT images of liver lesions, when contrasting with standard FCN methods. Moreover, when applying our 2D pipeline on a challenging 3D MRI prostate segmentation challenge we reach results that are competitive even when compared to 3D methods. The obtained results illustrate the strong potential and versatility of the pipeline by achieving highly accurate results on multi-modality images from different anatomical regions and organs.

2017-11-13

Medical Image Analysis (inconnu)

Actual: Actor-Critic Under Adversarial Learning

Nan Rosemary Ke

R Devon Hjelm

Generative Adversarial Networks (GANs) are a powerful framework for deep generative modeling. Posed as a two-player minimax problem, GANs ar… (voir plus)e typically trained end-to-end on real-valued data and can be used to train a generator of high-dimensional and realistic images. However, a major limitation of GANs is that training relies on passing gradients from the discriminator through the generator via back-propagation. This makes it fundamentally difficult to train GANs with discrete data, as generation in this case typically involves a non-differentiable function. These difficulties extend to the reinforcement learning setting when the action space is composed of discrete decisions. We address these issues by reframing the GAN framework so that the generator is no longer trained using gradients through the discriminator, but is instead trained using a learned critic in the actor-critic framework with a Temporal Difference (TD) objective. This is a natural fit for sequence modeling and we use it to achieve improvements on language modeling tasks over the standard Teacher-Forcing methods.

2017-11-12

ArXiv (prépublication)

Sparse Attentive Backtracking: Long-Range Credit Assignment in Recurrent Networks

Nan Rosemary Ke

A major drawback of backpropagation through time (BPTT) is the difficulty of learning long-term dependencies, coming from having to propagat… (voir plus)e credit information backwards through every single step of the forward computation. This makes BPTT both computationally impractical and biologically implausible. For this reason, full backpropagation through time is rarely used on long sequences, and truncated backpropagation through time is used as a heuristic. However, this usually leads to biased estimates of the gradient in which longer term dependencies are ignored. Addressing this issue, we propose an alternative algorithm, Sparse Attentive Backtracking, which might also be related to principles used by brains to learn long-term dependencies. Sparse Attentive Backtracking learns an attention mechanism over the hidden states of the past and selectively backpropagates through paths with high attention weights. This allows the model to learn long term dependencies while only backtracking for a small number of time steps, not just from the recent past but also from attended relevant past states.

2017-11-06

ArXiv (prépublication)