Publications

Successive-Cancellation Decoding of Reed-Muller Codes With Fast Hadamard Transform
Nghia Doan
Seyyed Ali Hashemi
Warren Gross
A novel permuted fast successive-cancellation list decoding algorithm with fast Hadamard transform (FHT-FSCL) is presented. The proposed dec… (voir plus)oder initializes
2021-08-28
ArXiv (preprint)
doi.org
arxiv.org
Approximate Bayesian Optimisation for Neural Networks
Nadhir Hassen
Irina Rish
2021-08-27
ArXiv (prépublication)
arxiv.org
arxiv.org
Extracting Weighted Automata for Approximate Minimization in Language Modelling
Clara Lacroce
Prakash Panangaden
Guillaume Rabusseau
2021-08-25
Proceedings of the Fifteenth International Conference on Grammatical Inference (publié)
proceedings.mlr.press
arxiv.org
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
2021-08-25
Proceedings of the Fourth Conference on Medical Imaging with Deep Learning (publié)
proceedings.mlr.press
openreview.net
Monitoring non-pharmaceutical public health interventions during the COVID-19 pandemic
Yannan Shen
Guido Powell
Iris Ganser
Qulu Zheng
Chris Grundy
Anya Okhmatovskaia
David Buckeridge
2021-08-24
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 Buckeridge
2021-08-23
Public Health Nutrition (publié)
doi.org
Magnetoencephalography resting-state correlates of executive and language components of verbal fluency
Victor Oswald
Younes Zerouali
Aubrée Boulet-Craig
M. Krajinovic
Caroline Laverdière
D. Sinnett
Pierre W. Jolicoeur
Sarah Lippé
Karim Jerbi
Philippe Robaey
2021-08-20
Scientific Reports (published)
doi.org
Lacking social support is associated with structural divergences in hippocampus–default network co-variation patterns
Chris Zajner
Nathan Spreng
Danilo Bzdok
Elaborate social interaction is a pivotal asset of the human species. The complexity of people’s social lives may constitute the dominatin… (voir plus)g factor in the vibrancy of many individuals’ environment. The neural substrates linked to social cognition thus appear especially susceptible when people endure periods of social isolation: here, we zoom in on the systematic inter-relationships between two such neural substrates, the allocortical hippocampus (HC) and the neocortical default network (DN). Previous human social neuroscience studies have focused on the DN, while HC subfields have been studied in most detail in rodents and monkeys. To bring into contact these two separate research streams, we directly quantified how DN subregions are coherently co-expressed with specific HC subfields in the context of social isolation. A two-pronged decomposition of structural brain scans from ∼40,000 UK Biobank participants linked lack of social support to mostly lateral subregions in the DN patterns. This lateral DN association co-occurred with HC patterns that implicated especially subiculum, presubiculum, CA2, CA3, and dentate gyrus. Overall, the subregion divergences within spatially overlapping signatures of HC-DN co-variation followed a clear segregation divide into the left and right brain hemispheres. Separable regimes of structural HC-DN co-variation also showed distinct associations with the genetic predisposition for lacking social support at the population level.
2021-08-19
bioRxiv (preprint)
doi.org
A modified Thompson sampling-based learning algorithm for unknown linear systems
Yi. Ouyang
Mukul Gagrani
Rahul Jain
We revisit the Thompson sampling-based learning algorithm for controlling an unknown linear system with quadratic cost proposed in [1]. This… (voir plus) algorithm operates in episodes of dynamic length and it is shown to have a regret bound of
2021-08-19
ArXiv (preprint)
doi.org
arxiv.org
Toward Optimal Solution for the Context-Attentive Bandit Problem
Djallel Bouneffouf
Raphael Feraud
Sohini Upadhyay
Irina Rish
Yasaman Khazaeni
2021-08-19
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (publié)
doi.org
Scalable Regret for Learning to Control Network-Coupled Subsystems With Unknown Dynamics
Sagar Sudhakara
Aditya Mahajan
Ashutosh Nayyar
Yi. Ouyang
In this article, we consider the problem of controlling an unknown linear quadratic Gaussian (LQG) system consisting of multiple subsystems … (voir plus)connected over a network. Our goal is to minimize and quantify the regret (i.e., loss in performance) of our learning and control strategy with respect to an oracle who knows the system model. Upfront viewing the interconnected subsystems globally and directly using existing LQG learning algorithms for the global system results in a regret that increases super-linearly with the number of subsystems. Instead, we propose a new Thompson sampling-based learning algorithm which exploits the structure of the underlying network. We show that the expected regret of the proposed algorithm is bounded by
2021-08-18
ArXiv (preprint)
doi.org
arxiv.org
Forgetting Enhances Episodic Control With Structured Memories
Annik Yalnizyan-Carson
Blake Richards
Forgetting is a normal process in healthy brains, and evidence suggests that the mammalian brain forgets more than is required based on limi… (voir plus)tations of mnemonic capacity. Episodic memories, in particular, are liable to be forgotten over time. Researchers have hypothesized that it may be beneficial for decision making to forget episodic memories over time. Reinforcement learning offers a normative framework in which to test such hypotheses. Here, we show that a reinforcement learning agent that uses an episodic memory cache to find rewards in maze environments can forget a large percentage of older memories without any performance impairments, if they utilize mnemonic representations that contain structural information about space. Moreover, we show that some forgetting can actually provide a benefit in performance compared to agents with unbounded memories. Our analyses of the agents show that forgetting reduces the influence of outdated information and states which are not frequently visited on the policies produced by the episodic control system. These results support the hypothesis that some degree of forgetting can be beneficial for decision making, which can help to explain why the brain forgets more than is required by capacity limitations.
2021-08-12
bioRxiv (preprint)
doi.org