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

Collaborateur·rice de recherche - Formerly McGill (but ending)

Paul Barde

Collaborateur·rice de recherche - McGill

Superviseur⋅e principal⋅e :

Maîtrise recherche - UdeM

Chris Beckham

Doctorat - Polytechnique

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

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Collaborateur·rice alumni - Polytechnique

Roger Girgis

Doctorat - Polytechnique

Florian Golemo

Postdoctorat - McGill

Maîtrise recherche - Polytechnique

Doctorat - UdeM

Co-superviseur⋅e :

Yousef Kotp

Maîtrise recherche - Concordia

Co-superviseur⋅e :

Maîtrise recherche - UdeM

Olga Luo

Doctorat - UdeM

Doctorat - UdeM

Joel Moniz

Doctorat - Polytechnique

Jonathan Pilault

Doctorat - Polytechnique

Juan Rodriguez

Doctorat - École de technologie suprérieure

Spécification directe du comportement par apprentissage par renforcement sous contrainte

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

Co-superviseur⋅e :

Doctorat - UdeM

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

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 data set consisting of real-world dialogues centered around recommendations. To address this issue and to facilitate our exploration here, we have collected ReDial, a data set 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.

Towards Text Generation with Adversarially Learned Neural Outlines

Sai Rajeswar

Adam Trischler

Recent progress in deep generative models has been fueled by two paradigms -- autoregressive and adversarial models. We propose a combinatio… (voir plus)n of both approaches with the goal of learning generative models of text. Our method first produces a high-level sentence outline and then generates words sequentially, conditioning on both the outline and the previous outputs. We generate outlines with an adversarial model trained to approximate the distribution of sentences in a latent space induced by general-purpose sentence encoders. This provides strong, informative conditioning for the autoregressive stage. Our quantitative evaluations suggests that conditioning information from generated outlines is able to guide the autoregressive model to produce realistic samples, comparable to maximum-likelihood trained language models, even at high temperatures with multinomial sampling. Qualitative results also demonstrate that this generative procedure yields natural-looking sentences and interpolations.

Twin Networks: Matching the Future for Sequence Generation

Nan Rosemary Ke

Adam Trischler

We propose a simple technique for encouraging generative RNNs to plan ahead. We train a "backward" recurrent network to generate a given seq… (voir plus)uence in reverse order, and we encourage states of the forward model to predict cotemporal states of the backward model. The backward network is used only during training, and plays no role during sampling or inference. We hypothesize that our approach eases modeling of long-term dependencies by implicitly forcing the forward states to hold information about the longer-term future (as contained in the backward states). We show empirically that our approach achieves 9% relative improvement for a speech recognition task, and achieves significant improvement on a COCO caption generation task.

2018-01-01

ICLR (Poster) (publié)

openreview.net

Unsupervised Depth Estimation, 3D Face Rotation and Replacement

Joel Ruben Antony Moniz

We present an unsupervised approach for learning to estimate three dimensional (3D) facial structure from a single image while also predicti… (voir plus)ng 3D viewpoint transformations that match a desired pose and facial geometry. We achieve this by inferring the depth of facial keypoints of an input image in an unsupervised manner, without using any form of ground-truth depth information. We show how it is possible to use these depths as intermediate computations within a new backpropable loss to predict the parameters of a 3D affine transformation matrix that maps inferred 3D keypoints of an input face to the corresponding 2D keypoints on a desired target facial geometry or pose. Our resulting approach, called DepthNets, can therefore be used to infer plausible 3D transformations from one face pose to another, allowing faces to be frontalized, transformed into 3D models or even warped to another pose and facial geometry. Lastly, we identify certain shortcomings with our formulation, and explore adversarial image translation techniques as a post-processing step to re-synthesize complete head shots for faces re-targeted to different poses or identities.

ACtuAL: Actor-Critic Under Adversarial Learning

Nan Rosemary Ke

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.

2017-11-13

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.

2017-11-07

ArXiv (prépublication)

Adversarial Generation of Natural Language

Sai Rajeswar

Generative Adversarial Networks (GANs) have gathered a lot of attention from the computer vision community, yielding impressive results for … (voir plus)image generation. Advances in the adversarial generation of natural language from noise however are not commensurate with the progress made in generating images, and still lag far behind likelihood based methods. In this paper, we take a step towards generating natural language with a GAN objective alone. We introduce a simple baseline that addresses the discrete output space problem without relying on gradient estimators and show that it is able to achieve state-of-the-art results on a Chinese poem generation dataset. We present quantitative results on generating sentences from context-free and probabilistic context-free grammars, and qualitative language modeling results. A conditional version is also described that can generate sequences conditioned on sentence characteristics.

2017-08-01

Proceedings of the 2nd Workshop on Representation Learning for NLP (publié)

doi.org

Self-organized Hierarchical Softmax

We propose a new self-organizing hierarchical softmax formulation for neural-network-based language models over large vocabularies. Instead … (voir plus)of using a predefined hierarchical structure, our approach is capable of learning word clusters with clear syntactical and semantic meaning during the language model training process. We provide experiments on standard benchmarks for language modeling and sentence compression tasks. We find that this approach is as fast as other efficient softmax approximations, while achieving comparable or even better performance relative to similar full softmax models.

2017-07-26

ArXiv (prépublication)

A Dataset and Exploration of Models for Understanding Video Data through Fill-in-the-Blank Question-Answering

Anna Rohrbach

While deep convolutional neural networks frequently approach or exceed human-level performance in benchmark tasks involving static images, e… (voir plus)xtending this success to moving images is not straightforward. Video understanding is of interest for many applications, including content recommendation, prediction, summarization, event/object detection, and understanding human visual perception. However, many domains lack sufficient data to explore and perfect video models. In order to address the need for a simple, quantitative benchmark for developing and understanding video, we present MovieFIB, a fill-in-the-blank question-answering dataset with over 300,000 examples, based on descriptive video annotations for the visually impaired. In addition to presenting statistics and a description of the dataset, we perform a detailed analysis of 5 different models predictions, and compare these with human performance. We investigate the relative importance of language, static (2D) visual features, and moving (3D) visual features, the effects of increasing dataset size, the number of frames sampled, and of vocabulary size. We illustrate that: this task is not solvable by a language model alone, our model combining 2D and 3D visual information indeed provides the best result, all models perform significantly worse than human-level. We provide human evaluation for responses given by different models and find that accuracy on the MovieFIB evaluation corresponds well with human judgment. We suggest avenues for improving video models, and hope that the MovieFIB challenge can be useful for measuring and encouraging progress in this very interesting field.

2017-07-21

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (publié)

doi.org

Unimodal Probability Distributions for Deep Ordinal Classification

Christopher Beckham

Chris Pal

Probability distributions produced by the cross-entropy loss for ordinal classification problems can possess undesired properties. We propos… (voir plus)e a straightforward technique to constrain discrete ordinal probability distributions to be unimodal via a combination of the Poisson probability mass function and the softmax nonlinearity. We evaluate this approach on two large ordinal image datasets and obtain promising results.

2017-07-17

Proceedings of the 34th International Conference on Machine Learning (publié)

proceedings.mlr.press