Publications

SPeCiaL: Self-Supervised Pretraining for Continual Learning

Lucas Caccia

Joelle Pineau

2021-06-16

ArXiv (preprint)

Comparative Study of Learning Outcomes for Online Learning Platforms

Francois St-Hilaire

Nathan J. Burns

Robert Belfer

Muhammad Shayan

Ariella Smofsky

Dung D. Vu

Antoine Frau

Joseph Potochny

Farid Faraji

Vincent Pavero

Neroli Ko

Ansona Onyi Ching

Sabina Elkins

A. Stepanyan

Adela Matajova

Laurent Charlin

Yoshua Bengio

Iulian V. Serban

Ekaterina Kochmar

2021-06-12

Lecture Notes in Computer Science (publié)

Incorporating dynamic flight network in SEIR to model mobility between populations

Xiaoye Ding

Shenyang Huang

Abby Leung

Reihaneh Rabbany

2021-06-10

Applied Network Science (publié)

RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting

Soumyasundar Pal

Liheng Ma

Yingxue Zhang

M. Coates

Spatio-temporal forecasting has numerous applications in analyzing wireless, traffic, and financial networks. Many classical statistical mod… (voir plus)els often fall short in handling the complexity and high non-linearity present in time-series data. Recent advances in deep learning allow for better modelling of spatial and temporal dependencies. While most of these models focus on obtaining accurate point forecasts, they do not characterize the prediction uncertainty. In this work, we consider the time-series data as a random realization from a nonlinear state-space model and target Bayesian inference of the hidden states for probabilistic forecasting. We use particle flow as the tool for approximating the posterior distribution of the states, as it is shown to be highly effective in complex, high-dimensional settings. Thorough experimentation on several real world time-series datasets demonstrates that our approach provides better characterization of uncertainty while maintaining comparable accuracy to the state-of-the art point forecasting methods.

2021-06-10

ArXiv (preprint)

Rapid simultaneous acquisition of macromolecular tissue volume, susceptibility, and relaxometry maps

Fang Frank Yu

Susie Y. Huang

T. Witzel

Ashwin S. Kumar

Congyu Liao

Tanguy Duval

Julien Cohen-Adad

Berkin Bilgic

Purpose A major obstacle to the clinical implementation of quantitative MR is the lengthy acquisition time required to derive multi-contrast… (voir plus) parametric maps. We sought to reduce the acquisition time for quantitative susceptibility mapping (QSM) and macromolecular tissue volume (MTV) by acquiring both contrasts simultaneously by leveraging their redundancies. The Joint Virtual Coil concept with generalized autocalibrating partially parallel acquisitions (JVC-GRAPPA) was applied to reduce acquisition time further. Methods Three adult volunteers were imaged on a 3T scanner using a multi-echo 3D GRE sequence acquired at three head orientations. MTV, QSM, R2*, T1, and proton density maps were reconstructed. The same sequence (GRAPPA R=4) was performed in subject #1 with a single head orientation for comparison. Fully sampled data was acquired in subject #2, from which retrospective undersampling was performed (R=6 GRAPPA and R=9 JVC-GRAPPA). Prospective undersampling was performed in subject #3 (R=6 GRAPPA and R=9 JVC-GRAPPA) using gradient blips to shift k-space sampling in later echoes. Results Subject #1’s multi-orientation and single-orientation MTV maps were not significantly different based on RMSE. For subject #2, the retrospectively undersampled JVC-GRAPPA and GRAPPA generated similar results as fully sampled data. This approach was validated with the prospectively undersampled images in subject #3. Using QSM, R2*, and MTV, the contributions of myelin and iron content to susceptibility was estimated. Conclusion We have developed a novel strategy to simultaneously acquire data for the reconstruction of five intrinsically co-registered 1-mm isotropic resolution multi-parametric maps, with a scan time of 6 minutes using JVC-GRAPPA.

2021-06-09

bioRxiv (preprint)

Understanding Capacity Saturation in Incremental Learning

Shenyang Huang

Vincent Francois-Lavet

Guillaume Rabusseau

2021-06-08

Canadian Conference on AI (publié)

Double-Linear Thompson Sampling for Context-Attentive Bandits

Djallel Bouneffouf

Raphael Feraud

Sohini Upadhyay

Yasaman Khazaeni

In this paper, we analyze and extend an online learning frame-work known as Context-Attentive Bandit, motivated by various practical applica… (voir plus)tions, from medical diagnosis to dialog systems, where due to observation costs only a small subset of a potentially large number of context variables can be observed at each iteration; however, the agent has a freedom to choose which variables to observe. We derive a novel algorithm, called Context-Attentive Thompson Sampling (CATS), which builds upon the Linear Thompson Sampling approach, adapting it to Context-Attentive Bandit setting. We provide a theoretical regret analysis and an extensive empirical evaluation demonstrating advantages of the proposed approach over several baseline methods on a variety of real-life datasets.

2021-06-06

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (publié)

Toward Skills Dialog Orchestration with Online Learning

Djallel Bouneffouf

Raphael Feraud

Sohini Upadhyay

Mayank Agarwal

Yasaman Khazaeni

Building multi-domain AI agents is a challenging task and an open problem in the area of AI. Within the domain of dialog, the ability to orc… (voir plus)hestrate multiple independently trained dialog agents, or skills, to create a unified system is of particular significance. In this work, we study the task of online posterior dialog orchestration, where we define posterior orchestration as the task of selecting a subset of skills which most appropriately answer a user input using features extracted from both the user input and the individual skills. To account for the various costs associated with extracting skill features, we consider online posterior orchestration under a skill execution budget. We formalize this setting as Context Attentive Bandit with Observations (CABO), a variant of context attentive bandits, and evaluate it on proprietary conversational datasets.

2021-06-06

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (publié)

Encoder-Decoder Neural Architecture Optimization for Keyword Spotting

Tong Mo

Bang Liu

2021-06-04

ArXiv (prépublication)

SAND-mask: An Enhanced Gradient Masking Strategy for the Discovery of Invariances in Domain Generalization

Soroosh Shahtalebi

Jean-Christophe Gagnon-Audet

Touraj Laleh

Mojtaba Faramarzi

Kartik Ahuja

A major bottleneck in the real-world applications of machine learning models is their failure in generalizing to unseen domains whose data d… (voir plus)istribution is not i.i.d to the training domains. This failure often stems from learning non-generalizable features in the training domains that are spuriously correlated with the label of data. To address this shortcoming, there has been a growing surge of interest in learning good explanations that are hard to vary, which is studied under the notion of Out-of-Distribution (OOD) Generalization. The search for good explanations that are \textit{invariant} across different domains can be seen as finding local (global) minimas in the loss landscape that hold true across all of the training domains. In this paper, we propose a masking strategy, which determines a continuous weight based on the agreement of gradients that flow in each edge of network, in order to control the amount of update received by the edge in each step of optimization. Particularly, our proposed technique referred to as"Smoothed-AND (SAND)-masking", not only validates the agreement in the direction of gradients but also promotes the agreement among their magnitudes to further ensure the discovery of invariances across training domains. SAND-mask is validated over the Domainbed benchmark for domain generalization and significantly improves the state-of-the-art accuracy on the Colored MNIST dataset while providing competitive results on other domain generalization datasets.

2021-06-04

ArXiv (prépublication)

Continual Learning in Deep Networks: an Analysis of the Last Layer

Timothee LESORT

Thomas George