Publications

Routing policy choice prediction in a stochastic network: Recursive model and solution algorithm
Tien Mai
Xinlian Yu
Song Gao
Emma Frejinger
2021-08-31
Transportation Research Part B: Methodological (publié)
doi.org
A Survey of Exploration Methods in Reinforcement Learning
Susan Amin
Maziar Gomrokchi
Harsh Satija
Herke van Hoof
Doina Precup
2021-08-31
ArXiv (prépublication)
arxiv.org
arxiv.org
Problems in the deployment of machine-learned models in health care
Joseph Paul Cohen
Tianshi Cao
Joseph D Viviano
Chinwei Huang
Michael Fralick
Marzyeh Ghassemi
M. Mamdani
R. Greiner
Yoshua Bengio
2021-08-29
Canadian Medical Association Journal (publié)
doi.org
A Data Mining Analysis of Cross-Regional Study of Apparel Consumption
Osmud Rahman
Benjamin C. M. Fung
2021-08-27
Comprehensible Science (publié)
doi.org
Approximate Bayesian Optimisation for Neural Networks
Nadhir Hassen
Irina Rish
2021-08-26
ArXiv (prépublication)
arxiv.org
arxiv.org
Extracting Weighted Automata for Approximate Minimization in Language Modelling
Clara Lacroce
Prakash Panangaden
Guillaume Rabusseau
In this paper we study the approximate minimization problem for language modelling. We assume we are given some language model as a black bo… (voir plus)x. The objective is to obtain a weighted finite automaton (WFA) that fits within a given size constraint and which mimics the behaviour of the original model while minimizing some notion of distance between the black box and the extracted WFA. We provide an algorithm for the approximate minimization of black boxes trained for language modelling of sequential data over a one-letter alphabet. By reformulating the problem in terms of Hankel matrices, we leverage classical results on the approximation of Hankel operators, namely the celebrated Adamyan-Arov-Krein (AAK) theory. This allows us to use the spectral norm to measure the distance between the black box and the WFA. We provide theoretical guarantees to study the potentially infinite-rank Hankel matrix of the black box, without accessing the training data, and we prove that our method returns an asymptotically-optimal approximation.
2021-08-24
Proceedings of the Fifteenth International Conference on Grammatical Inference (publié)
doi.org
proceedings.mlr.press
HAD-Net: A Hierarchical Adversarial Knowledge Distillation Network for Improved Enhanced Tumour Segmentation Without Post-Contrast Images
Saverio Vadacchino
Raghav Mehta
Nazanin Mohammadi Sepahvand
Brennan Nichyporuk
James J Clark
Tal Arbel
Segmentation of enhancing tumours or lesions from MRI is important for detecting new disease activity in many clinical contexts. However, ac… (voir plus)curate segmentation requires the inclusion of medical images (e.g., T1 post contrast MRI) acquired after injecting patients with a contrast agent (e.g., Gadolinium), a process no longer thought to be safe. Although a number of modality-agnostic segmentation networks have been developed over the past few years, they have been met with limited success in the context of enhancing pathology segmentation. In this work, we present HAD-Net, a novel offline adversarial knowledge distillation (KD) technique, whereby a pre-trained teacher segmentation network, with access to all MRI sequences, teaches a student network, via hierarchical adversarial training, to better overcome the large domain shift presented when crucial images are absent during inference. In particular, we apply HAD-Net to the challenging task of enhancing tumour segmentation when access to post-contrast imaging is not available. The proposed network is trained and tested on the BraTS 2019 brain tumour segmentation challenge dataset, where it achieves performance improvements in the ranges of 16% - 26% over (a) recent modality-agnostic segmentation methods (U-HeMIS, U-HVED), (b) KD-Net adapted to this problem, (c) the pre-trained student network and (d) a non-hierarchical version of the network (AD-Net), in terms of Dice scores for enhancing tumour (ET). The network also shows improvements in tumour core (TC) Dice scores. Finally, the network outperforms both the baseline student network and AD-Net in terms of uncertainty quantification for enhancing tumour segmentation based on the BraTs 2019 uncertainty challenge metrics. Our code is publicly available at: https://github.com/SaverioVad/HAD_Net
2021-08-24
Proceedings of the Fourth Conference on Medical Imaging with Deep Learning (publié)
doi.org
proceedings.mlr.press
Monitoring non-pharmaceutical public health interventions during the COVID-19 pandemic
Yannan Shen
Guido Powell
Iris Ganser
Qulu Zheng
Chris Grundy
Anya Okhmatovskaia
David L. Buckeridge
Measuring and monitoring non-pharmaceutical interventions is important yet challenging due to the need to clearly define and encode non-phar… (voir plus)maceutical interventions, to collect geographically and socially representative data, and to accurately document the timing at which interventions are initiated and changed. These challenges highlight the importance of integrating and triangulating across multiple databases and the need to expand and fund the mandate for public health organizations to track interventions systematically.
2021-08-23
Scientific Data (publié)
doi.org
Generating community measures of food purchasing activities using store-level electronic grocery transaction records: an ecological study in Montreal, Canada
Hiroshi Mamiya
Alexandra M. Schmidt
Erica E.M. Moodie
Yu Ma
David L Buckeridge
2021-08-22
Public Health Nutrition (publié)
doi.org
Deep Learning for Detecting Extreme Weather Patterns
Mayur Mudigonda
Mayur Mudigonda, Prabhat Ram
Prabhat Ram
Karthik Kashinath
Evan Racah
Ankur Mahesh
Yunjie Liu
Christopher Beckham
Jim Biard
Thorsten Kurth
Sookyung Kim
S Ebrahimi Kahou
Tegan Maharaj
Burlen Loring
Christopher Pal
Travis O'Brien
K. Kunkel
Kenneth E. Kunkel
M. Wehner
Michael F. Wehner … (voir 2 de plus)
W. Collins
William D. Collins
2021-08-19
Deep Learning for the Earth Sciences (publié)
doi.org
Loneliness is linked to specific subregional alterations in hippocampus-default network co-variation
Chris Zajner
R. Nathan Spreng
Danilo Bzdok
Social interaction complexity makes humans unique. But in times of social deprivation this strength risks to expose important vulnerabilitie… (voir plus)s. Human social neuroscience studies have placed a premium on the default network (DN). In contrast, hippocampus (HC) subfields have been intensely studied in rodents and monkeys. To bridge these two literatures, we here quantified how DN subregions systematically co-vary with specific HC subfields in the context of subjective social isolation (i.e., loneliness). By co-decomposition using structural brain scans of ∼40,000 UK Biobank participants, loneliness was specially linked to midline subregions in the uncovered DN patterns. These association cortex signatures coincided with concomitant HC patterns implicating especially CA1 and molecular layer. These patterns also showed a strong affiliation with the fornix white-matter tract and the nucleus accumbens. In addition, separable signatures of structural HC-DN co-variation had distinct associations with the genetic predisposition for loneliness at the population level.
2021-08-18
bioRxiv (prépublication)
doi.org
Toward Optimal Solution for the Context-Attentive Bandit Problem
Djallel Bouneffouf
Raphael Feraud
Sohini Upadhyay
Irina Rish
Yasaman Khazaeni
2021-08-18
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (publié)
doi.org