Publications

Image-Guided Brachytherapy for Rectal Cancer: Reviewing the Past Two Decades of Clinical Investigation
T. Vuong
Aurélie Garant
Veronique Vendrely
Remi Nout
André-Guy Martin
S. Enger
Ervin Podgorsak
Belal Moftah
S. Devic
2022-09-30
Cancers (published)
doi.org
Interpolated Adversarial Training: Achieving Robust Neural Networks Without Sacrificing Too Much Accuracy
Alex Lamb
Vikas Verma
Kenji Kawaguchi
Juho Kannala
Alexander Matyasko
Yoshua Bengio
Savya Khosla
Adversarial robustness has become a central goal in deep learning, both in theory and in practice. However, successful methods to improve th… (see more)e adversarial robustness (such as adversarial training) greatly hurt generalization performance on the unperturbed data. This could have a major impact on how achieving adversarial robustness affects real world systems (i.e. many may opt to forego robustness if it can improve accuracy on the unperturbed data). We propose Interpolated Adversarial Training, which employs recently proposed interpolation based training methods in the framework of adversarial training. On CIFAR-10, adversarial training increases the standard test error (when there is no adversary) from 4.43% to 12.32%, whereas with our Interpolated adversarial training we retain adversarial robustness while achieving a standard test error of only 6.45%. With our technique, the relative increase in the standard error for the robust model is reduced from 178.1% to just 45.5%.
2022-09-30
Neural Networks (published)
doi.org
arxiv.org
Lifelong Topological Visual Navigation
Rey Reza Wiyatno
Anqi Xu
Liam Paull
Commonly, learning-based topological navigation approaches produce a local policy while preserving some loose connectivity of the space thro… (see more)ugh a topological map. Nevertheless, spurious or missing edges in the topological graph often lead to navigation failure. In this work, we propose a sampling-based graph building method, which results in sparser graphs yet with higher navigation performance compared to baseline methods. We also propose graph maintenance strategies that eliminate spurious edges and expand the graph as needed, which improves lifelong navigation performance. Unlike controllers that learn from fixed training environments, we show that our model can be fine-tuned using only a small number of collected trajectory images from a real-world environment where the agent is deployed. We demonstrate successful navigation after fine-tuning on real-world environments, and notably show significant navigation improvements over time by applying our lifelong graph maintenance strategies.
2022-09-30
IEEE Robotics and Automation Letters (published)
doi.org
arxiv.org
MixEHR-Guided: A guided multi-modal topic modeling approach for large-scale automatic phenotyping using the electronic health record
Yuri Ahuja
Yuesong Zou
Aman Verma
David L Buckeridge
Yuemei Li
2022-09-30
Journal of Biomedical Informatics (published)
doi.org
Social isolation and the brain in the pandemic era
Danilo Bzdok
Robin I. M. Dunbar
2022-09-30
Nature Human Behaviour (published)
doi.org
Synthetic data as an enabler for machine learning applications in medicine
Jean-Francois Rajotte
Robert Bergen
David L Buckeridge
Khaled El Emam
Raymond Ng
Elissa Strome
2022-09-30
iScience (published)
doi.org
The use of artificial intelligence and virtual reality in doctor-patient risk communication: A scoping review.
Ryan Antel
S. A. Rahimi
Elena Guadagno
Jason M. Harley
Dan Poenaru
2022-09-30
Patient Education and Counseling (published)
doi.org
Evolution of cell size control is canalized towards adders or sizers by cell cycle structure and selective pressures
Felix Proulx-Giraldeau
Jan M Skotheim
Paul François
Cell size is controlled to be within a specific range to support physiological function. To control their size, cells use diverse mechanisms… (see more) ranging from ‘sizers’, in which differences in cell size are compensated for in a single cell division cycle, to ‘adders’, in which a constant amount of cell growth occurs in each cell cycle. This diversity raises the question why a particular cell would implement one rather than another mechanism? To address this question, we performed a series of simulations evolving cell size control networks. The size control mechanism that evolved was influenced by both cell cycle structure and specific selection pressures. Moreover, evolved networks recapitulated known size control properties of naturally occurring networks. If the mechanism is based on a G1 size control and an S/G2/M timer, as found for budding yeast and some human cells, adders likely evolve. But, if the G1 phase is significantly longer than the S/G2/M phase, as is often the case in mammalian cells in vivo, sizers become more likely. Sizers also evolve when the cell cycle structure is inverted so that G1 is a timer, while S/G2/M performs size control, as is the case for the fission yeast S. pombe. For some size control networks, cell size consistently decreases in each cycle until a burst of cell cycle inhibitor drives an extended G1 phase much like the cell division cycle of the green algae Chlamydomonas. That these size control networks evolved such self-organized criticality shows how the evolution of complex systems can drive the emergence of critical processes.
2022-09-29
eLife (published)
doi.org
Learning Robust Kernel Ensembles with Kernel Average Pooling
Pouya Bashivan
Adam Ibrahim
Amirozhan Dehghani
Yifei Ren
2022-09-29
ArXiv (preprint)
doi.org
openreview.net
SPeCiaL: Self-Supervised Pretraining for Continual Learning
Lucas Caccia
Joelle Pineau
2022-09-27
Continual Semi-Supervised Learning (published)
doi.org
arxiv.org
From analytic to synthetic-organizational pluralisms: A pluralistic enactive psychiatry
Christophe Gauld
Kristopher Nielsen
Manon Job
Hugo Bottemanne
Guillaume Dumas
Reliance on sole reductionism, whether explanatory, methodological or ontological, is difficult to support in clinical psychiatry. Rather, p… (see more)sychiatry is challenged by a plurality of approaches. There exist multiple legitimate ways of understanding human functionality and disorder, i.e., different systems of representation, different tools, different methodologies and objectives. Pluralistic frameworks have been presented through which the multiplicity of approaches in psychiatry can be understood. In parallel of these frameworks, an enactive approach for psychiatry has been proposed. In this paper, we consider the relationships between the different kinds of pluralistic frameworks and this enactive approach for psychiatry. We compare the enactive approach in psychiatry with wider analytical forms of pluralism. On one side, the enactive framework anchored both in cognitive sciences, theory of dynamic systems, systems biology, and phenomenology, has recently been proposed as an answer to the challenge of an integrative psychiatry. On the other side, two forms of explanatory pluralisms can be described: a non-integrative pluralism and an integrative pluralism. The first is tolerant, it examines the coexistence of different potentially incompatible or untranslatable systems in the scientific or clinical landscape. The second is integrative and proposes to bring together the different levels of understanding and systems of representations. We propose that enactivism is inherently a form of integrative pluralism, but it is at the same time a component of the general framework of explanatory pluralism, composed of a set of so-called analytical approaches. A significant number of mental health professionals are already accepting the variety of clinical and scientific approaches. In this way, a rigorous understanding of the theoretical positioning of psychiatric actors seems necessary to promote quality clinical practice. The study of entanglements between an analytical pluralism and a synthetic-organizational enactivist pluralism could prove fruitful.
2022-09-26
Frontiers in Psychiatry (published)
doi.org
FedShuffle: Recipes for Better Use of Local Work in Federated Learning
Samuel Horváth
Maziar Sanjabi
Lin Xiao
Peter Richtárik
Michael G. Rabbat
The practice of applying several local updates before aggregation across clients has been empirically shown to be a successful approach to o… (see more)vercoming the communication bottleneck in Federated Learning (FL). Such methods are usually implemented by having clients perform one or more epochs of local training per round while randomly reshuffling their finite dataset in each epoch. Data imbalance, where clients have different numbers of local training samples, is ubiquitous in FL applications, resulting in different clients performing different numbers of local updates in each round. In this work, we propose a general recipe, FedShuffle, that better utilizes the local updates in FL, especially in this regime encompassing random reshuffling and heterogeneity. FedShuffle is the first local update method with theoretical convergence guarantees that incorporates random reshuffling, data imbalance, and client sampling — features that are essential in large-scale cross-device FL. We present a comprehensive theoretical analysis of FedShuffle and show, both theoretically and empirically, that it does not suffer from the objective function mismatch that is present in FL methods that assume homogeneous updates in heterogeneous FL setups, such as FedAvg (McMahan et al., 2017). In addition, by combining the ingredients above, FedShuffle improves upon FedNova (Wang et al., 2020), which was previously proposed to solve this mismatch. Similar to Mime (Karimireddy et al., 2020), we show that FedShuffle with momentum variance reduction (Cutkosky & Orabona, 2019) improves upon non-local methods under a Hessian similarity assumption.
2022-09-25
TMLR (accepted)
doi.org
openreview.net