Aaron Courville

Anirudh Buvanesh

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Laurent Charlin

Razvan Ciuca

Maîtrise recherche - Université de Montréal

Juan Duque

Doctorat - UdeM

Doctorat - UdeM

Doctorat - UdeM

Uday Kapur

Doctorat - UdeM

Amr Khalifa

Doctorat - UdeM

Samuel Lavoie

Doctorat - UdeM

Zhixuan Lin

Doctorat - UdeM

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Doctorat - UdeM

Johan Samir Obando Ceron

Doctorat - UdeM

Co-superviseur⋅e :

Collaborateur·rice de recherche - UdeM

Dereck Piché

Maîtrise recherche - UdeM

Khaled Rouissi

Maîtrise recherche - UdeM

Esra'a Saleh

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Glen Berseth

Google Scholar

Vedant Shah

Doctorat - UdeM

Doctorat - UdeM

Yusong Wu

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Anna (Cheng-Zhi) Huang

Sujin yun

Doctorat - UdeM

Doctorat - UdeM

Doctorat - UdeM

Co-superviseur⋅e :

Yoshua Bengio

Publications

AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation

Jae Hyun Lim

Christopher Pal

Entropy is ubiquitous in machine learning, but it is in general intractable to compute the entropy of the distribution of an arbitrary conti… (voir plus)nuous random variable. In this paper, we propose the amortized residual denoising autoencoder (AR-DAE) to approximate the gradient of the log density function, which can be used to estimate the gradient of entropy. Amortization allows us to significantly reduce the error of the gradient approximator by approaching asymptotic optimality of a regular DAE, in which case the estimation is in theory unbiased. We conduct theoretical and experimental analyses on the approximation error of the proposed method, as well as extensive studies on heuristics to ensure its robustness. Finally, using the proposed gradient approximator to estimate the gradient of entropy, we demonstrate state-of-the-art performance on density estimation with variational autoencoders and continuous control with soft actor-critic.

2020-11-20

Proceedings of the 37th International Conference on Machine Learning (publié)

proceedings.mlr.press

Countering Language Drift with Seeded Iterated Learning

Yuchen Lu

Soumye Singhal

Florian Strub

Olivier Pietquin

Pretraining on human corpus and then finetuning in a simulator has become a standard pipeline for training a goal-oriented dialogue agent. N… (voir plus)evertheless, as soon as the agents are finetuned to maximize task completion, they suffer from the so-called language drift phenomenon: they slowly lose syntactic and semantic properties of language as they only focus on solving the task. In this paper, we propose a generic approach to counter language drift called Seeded iterated learning (SIL). We periodically refine a pretrained student agent by imitating data sampled from a newly generated teacher agent. At each time step, the teacher is created by copying the student agent, before being finetuned to maximize task completion. SIL does not require external syntactic constraint nor semantic knowledge, making it a valuable task-agnostic finetuning protocol. We evaluate SIL in a toy-setting Lewis Game, and then scale it up to the translation game with natural language. In both settings, SIL helps counter language drift as well as it improves the task completion compared to baselines.

2020-11-20

Proceedings of the 37th International Conference on Machine Learning (publié)

proceedings.mlr.press

Recursive Top-Down Production for Sentence Generation with Latent Trees

Timothy J. O'Donnell

We model the recursive production property of context-free grammars for natural and synthetic languages. To this end, we present a dynamic p… (voir plus)rogramming algorithm that marginalises over latent binary tree structures with

2020-10-31

Findings of the Association for Computational Linguistics: EMNLP 2020 (publié)

Supervised Seeded Iterated Learning for Interactive Language Learning

Yuchen Lu

Soumye Singhal

Florian Strub

Olivier Pietquin

2020-10-31

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (publié)

NU-GAN: High resolution neural upsampling with GAN

In this paper, we propose NU-GAN, a new method for resampling audio from lower to higher sampling rates (upsampling). Audio upsampling is an… (voir plus) important problem since productionizing generative speech technology requires operating at high sampling rates. Such applications use audio at a resolution of 44.1 kHz or 48 kHz, whereas current speech synthesis methods are equipped to handle a maximum of 24 kHz resolution. NU-GAN takes a leap towards solving audio upsampling as a separate component in the text-to-speech (TTS) pipeline by leveraging techniques for audio generation using GANs. ABX preference tests indicate that our NU-GAN resampler is capable of resampling 22 kHz to 44.1 kHz audio that is distinguishable from original audio only 7.4% higher than random chance for single speaker dataset, and 10.8% higher than chance for multi-speaker dataset.

2020-10-21

ArXiv (prépublication)

Explicitly Modeling Syntax in Language Model improves Generalization

Syntax is fundamental to our thinking about language. Although neural networks are very successful in many tasks, they do not explicitly mod… (voir plus)el syntactic structure. Failing to capture the structure of inputs could lead to generalization problems and over-parametrization. In the present work, we propose a new syntax-aware language model: Syntactic Ordered Memory (SOM). The model explicitly models the structure with a one-step look-ahead parser and maintains the conditional probability setting of the standard language model. Experiments show that SOM can achieve strong results in language modeling and syntactic generalization tests, while using fewer parameters then other models.

2020-10-20

arXiv.org (prépublication)

dblp.uni-trier.de

A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM

Iulian V. Serban

Varun Gupta

Ekaterina Kochmar

Dung D. Vu

Robert Belfer

2020-06-09

Artificial Intelligence in Education (publié)

Stochastic Neural Network with Kronecker Flow

Gintare Karolina Dziugaite

Ahmed Touati

Pascal Vincent

Alexandre Lacoste

Recent advances in variational inference enable the modelling of highly structured joint distributions, but are limited in their capacity to… (voir plus) scale to the high-dimensional setting of stochastic neural networks. This limitation motivates a need for scalable parameterizations of the noise generation process, in a manner that adequately captures the dependencies among the various parameters. In this work, we address this need and present the Kronecker Flow, a generalization of the Kronecker product to invertible mappings designed for stochastic neural networks. We apply our method to variational Bayesian neural networks on predictive tasks, PAC-Bayes generalization bound estimation, and approximate Thompson sampling in contextual bandits. In all setups, our methods prove to be competitive with existing methods and better than the baselines.

2020-06-02

Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (publié)

proceedings.mlr.press

Detecting semantic anomalies

Faruk Ahmed

We critically appraise the recent interest in out-of-distribution (OOD) detection and question the practical relevance of existing benchmark… (voir plus)s. While the currently prevalent trend is to consider different datasets as OOD, we argue that out-distributions of practical interest are ones where the distinction is semantic in nature for a specified context, and that evaluative tasks should reflect this more closely. Assuming a context of object recognition, we recommend a set of benchmarks, motivated by practical applications. We make progress on these benchmarks by exploring a multi-task learning based approach, showing that auxiliary objectives for improved semantic awareness result in improved semantic anomaly detection, with accompanying generalization benefits.

2020-04-02

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

Pix2Shape – Towards Unsupervised Learning of 3D Scenes from Images using a View-based Representation

Sai Rajeswar

Fahim Mannan

Florian Golemo

Jérôme Parent-lévesque

David Vázquez

Derek Nowrouzezahrai

We infer and generate three-dimensional (3D) scene information from a single input image and without supervision. This problem is under-expl… (voir plus)ored, with most prior work relying on supervision from, e.g., 3D ground-truth, multiple images of a scene, image silhouettes or key-points. We propose Pix2Shape, an approach to solve this problem with four components: (i) an encoder that infers the latent 3D representation from an image, (ii) a decoder that generates an explicit 2.5D surfel-based reconstruction of a scene from the latent code (iii) a differentiable renderer that synthesizes a 2D image from the surfel representation, and (iv) a critic network trained to discriminate between images generated by the decoder-renderer and those from a training distribution. Pix2Shape can generate complex 3D scenes that scale with the view-dependent on-screen resolution, unlike representations that capture world-space resolution, i.e., voxels or meshes. We show that Pix2Shape learns a consistent scene representation in its encoded latent space and that the decoder can then be applied to this latent representation in order to synthesize the scene from a novel viewpoint. We evaluate Pix2Shape with experiments on the ShapeNet dataset as well as on a novel benchmark we developed, called 3D-IQTT, to evaluate models based on their ability to enable 3d spatial reasoning. Qualitative and quantitative evaluation demonstrate Pix2Shape's ability to solve scene reconstruction, generation, and understanding tasks.

2020-03-19

International Journal of Computer Vision (inconnu)

Solving ODE with Universal Flows: Approximation Theory for Flow-Based Models

Laurent Dinh

Normalizing flows are powerful invertible probabilistic models that can be used to translate two probability distributions, in a way that al… (voir plus)lows us to efficiently track the change of probability density. However, to trade for computational efficiency in sampling and in evaluating the log-density, special parameterization designs have been proposed at the cost of representational expressiveness. In this work, we propose to use ODEs as a framework to establish universal approximation theory for certain families of flow-based models.

2020-02-25

International Conference on Learning Representations (publié)

openreview.net

Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models

Laurent Dinh

In this work, we propose a new family of generative flows on an augmented data space, with an aim to improve expressivity without drasticall… (voir plus)y increasing the computational cost of sampling and evaluation of a lower bound on the likelihood. Theoretically, we prove the proposed flow can approximate a Hamiltonian ODE as a universal transport map. Empirically, we demonstrate state-of-the-art performance on standard benchmarks of flow-based generative modeling.

2020-02-16

ArXiv (prépublication)