Publications

Measures of balance in combinatorial optimization
Philippe Olivier
Andrea Lodi
G. Pesant
2021-06-25
4OR (published)
doi.org
Measures of balance in combinatorial optimization
Philippe Olivier
Andrea Lodi
Gilles Pesant
2021-06-25
4OR (publié)
doi.org
A parsimonious description of global functional brain organization in three spatiotemporal patterns
Taylor Bolt
Jason S. Nomi
Danilo Bzdok
Jorge A. Salas
Catie Chang
B. T. Thomas Yeo
Lucina Q. Uddin
S. Keilholz
2021-06-20
Nature Neuroscience (published)
doi.org
A Data Mining Analysis of Cross-Regional Study of Apparel Consumption
Osmud Rahman
Benjamin Fung
2021-06-19
(published)
doi.org
Smart about medications (SAM): a digital solution to enhance medication management following hospital discharge
Santiago Márquez Fosser
Nadar Mahmoud
Bettina Habib
Daniala L Weir
Fiona Chan
Rola El Halabieh
Jeanne Vachon
Manish Thakur
Thai Tran
Melissa Bustillo
Caroline Beauchamp
André Bonnici
David Buckeridge
Robyn Tamblyn
2021-06-18
JAMIA Open (publié)
doi.org
SPeCiaL: Self-Supervised Pretraining for Continual Learning
Lucas Caccia
Joelle Pineau
2021-06-16
ArXiv (preprint)
doi.org
arxiv.org
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)
doi.org
arxiv.org
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)
doi.org
arxiv.org
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)
arxiv.org
arxiv.org
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)
doi.org
Understanding Capacity Saturation in Incremental Learning
Shenyang Huang
Vincent Francois-Lavet
Guillaume Rabusseau
2021-06-08
Canadian Conference on AI (publié)
doi.org
Double-Linear Thompson Sampling for Context-Attentive Bandits
Djallel Bouneffouf
Raphael Feraud
Sohini Upadhyay
Yasaman Khazaeni
Irina Rish
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)
doi.org
arxiv.org