Publications

Dynamic shimming in the cervical spinal cord for multi-echo gradient-echo imaging at 3 T
Eva Alonso‐Ortiz
Daniel Papp
Alain D’astous
Julien Cohen-Adad
2021-07-21
ArXiv (preprint)
doi.org
arxiv.org
Parametric Scattering Networks
Shanel Gauthier
Benjamin Th'erien
Laurent Alséne-Racicot
Irina Rish
Eugene Belilovsky
Michael Eickenberg
Guy Wolf
The wavelet scattering transform creates geometric in-variants and deformation stability. In multiple signal do-mains, it has been shown to … (voir plus)yield more discriminative rep-resentations compared to other non-learned representations and to outperform learned representations in certain tasks, particularly on limited labeled data and highly structured signals. The wavelet filters used in the scattering trans-form are typically selected to create a tight frame via a pa-rameterized mother wavelet. In this work, we investigate whether this standard wavelet filterbank construction is op-timal. Focusing on Morlet wavelets, we propose to learn the scales, orientations, and aspect ratios of the filters to produce problem-specific parameterizations of the scattering transform. We show that our learned versions of the scattering transform yield significant performance gains in small-sample classification settings over the standard scat-tering transform. Moreover, our empirical results suggest that traditional filterbank constructions may not always be necessary for scattering transforms to extract effective rep-resentations.
2021-07-20
ArXiv (preprint)
doi.org
arxiv.org
On generalized surrogate duality in mixed-integer nonlinear programming
Benjamin Muller
Gonzalo Munoz
Maxime Gasse
Ambros Gleixner
Andrea Lodi
Felipe Serrano
2021-07-17
Mathematical Programming (publié)
doi.org
arxiv.org
Symptom network analysis of the sleep disorders diagnostic criteria based on the clinical text of the ICSD‐3
Christophe Gauld
Régis Lopez
Charles Morin
Pierre A. GEOFFROY
Julien Maquet
Pierre Desvergnes
Aileen McGonigal
Yves Dauvilliers
Pierre Philip
Guillaume Dumas
Jean‐Arthur Micoulaud‐Franchi
2021-07-16
Journal of Sleep Research (publié)
doi.org
Symptom network analysis of the sleep disorders diagnostic criteria based on the clinical text of the ICSD‐3
Christophe Gauld
Régis Lopez
C. Morin
Pierre A. GEOFFROY
Julien Maquet
Pierre Desvergnes
Aileen McGonigal
Yves A. Dauvilliers
Pierre Philip
Guillaume Dumas
J-a Micoulaud-franchi
The third edition of the International Classification of Sleep Disorders (ICSD‐3) is the authoritative clinical text for the diagnosis of … (voir plus)sleep disorders. An important issue of sleep nosology is to better understand the relationship between symptoms found in conventional diagnostic manuals and to compare classifications. Nevertheless, to our knowledge, there is no specific exhaustive work on the general structure of the networks of symptoms of sleep disorders as described in diagnostic manuals. The general aim of the present study was to use symptom network analysis to explore the diagnostic criteria in the ICSD‐3 manual. The ICSD‐3 diagnostic criteria related to clinical manifestations were systematically identified, and the units of analysis (symptoms) were labelled from these clinical manifestation diagnostic criteria using three rules (“Conservation”, “Splitting”, “Lumping”). A total of 37 of the 43 main sleep disorders with 160 units of analysis from 114 clinical manifestations in the ICSD‐3 were analysed. A symptom network representing all individual ICSD‐3 criteria and connections between them was constructed graphically (network estimation), quantified with classical metrics (network inference with global and local measures) and tested for robustness. The global measure of the sleep symptoms network shows that it can be considered as a small world, suggesting a strong interconnection between symptoms in the ICSD‐3. Local measures show the central role of three kinds of bridge sleep symptoms: daytime sleepiness, insomnia, and behaviour during sleep symptoms. Such a symptom network analysis of the ICSD‐3 structure could provide a framework for better systematising and organising symptomatology in sleep medicine.
2021-07-16
Journal of Sleep Research (published)
doi.org
Design of Hesitation Gestures for Nonverbal Human-Robot Negotiation of Conflicts
AJung Moon
Maneezhay Hashmi
H. F. Machiel Van Der Loos
Elizabeth A. Croft
Aude Billard
When the question of who should get access to a communal resource first is uncertain, people often negotiate via nonverbal communication to … (voir plus)resolve the conflict. What should a robot be programmed to do when such conflicts arise in Human-Robot Interaction? The answer to this question varies depending on the context of the situation. Learning from how humans use hesitation gestures to negotiate a solution in such conflict situations, we present a human-inspired design of nonverbal hesitation gestures that can be used for Human-Robot Negotiation. We extracted characteristic features of such negotiative hesitations humans use, and subsequently designed a trajectory generator (Negotiative Hesitation Generator) that can re-create the features in robot responses to conflicts. Our human-subjects experiment demonstrates the efficacy of the designed robot behaviour against non-negotiative stopping behaviour of a robot. With positive results from our human-robot interaction experiment, we provide a validated trajectory generator with which one can explore the dynamics of human-robot nonverbal negotiation of resource conflicts.
2021-07-11
ACM Transactions on Human-Robot Interaction (publié)
doi.org
TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion
Jiapeng Wu
Yishi Xu
Yingxue Zhang
Chen Ma
Mark Coates
Jackie Cheung
Reasoning in a temporal knowledge graph (TKG) is a critical task for information retrieval and semantic search. It is particularly challengi… (voir plus)ng when the TKG is updated frequently. The model has to adapt to changes in the TKG for efficient training and inference while preserving its performance on historical knowledge. Recent work approaches TKG completion (TKGC) by augmenting the encoder-decoder framework with a time-aware encoding function. However, naively fine-tuning the model at every time step using these methods does not address the problems of 1) catastrophic forgetting, 2) the model's inability to identify the change of facts (e.g., the change of the political affiliation and end of a marriage), and 3) the lack of training efficiency. To address these challenges, we present the Time-aware Incremental Embedding (TIE) framework, which combines TKG representation learning, experience replay, and temporal regularization. We introduce a set of metrics that characterizes the intransigence of the model and propose a constraint that associates the deleted facts with negative labels. Experimental results on Wikidata12k and YAGO11k datasets demonstrate that the proposed TIE framework reduces training time by about ten times and improves on the proposed metrics compared to vanilla full-batch training. It comes without a significant loss in performance for any traditional measures. Extensive ablation studies reveal performance trade-offs among different evaluation metrics, which is essential for decision-making around real-world TKG applications.
2021-07-11
Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (publié)
doi.org
arxiv.org
Parallel and Recurrent Cascade Models as a Unifying Force for Understanding Subcellular Computation
Emerson F. Harkin
Peter R. Shen
Anisha Goel
Blake Richards
Richard Naud
Neurons are very complicated computational devices, incorporating numerous non-linear processes, particularly in their dendrites. Biophysica… (voir plus)l models capture these processes directly by explicitly modelling physiological variables, such as ion channels, current flow, membrane capacitance, etc. However, another option for capturing the complexities of real neural computation is to use cascade models, which treat individual neurons as a cascade of linear and non-linear operations, akin to a multi-layer artificial neural network. Recent research has shown that cascade models can capture single-cell computation well, but there are still a number of sub-cellular, regenerative dendritic phenomena that they cannot capture, such as the interaction between sodium, calcium, and NMDA spikes in different compartments. Here, we propose that it is possible to capture these additional phenomena using parallel, recurrent cascade models, wherein an individual neuron is modelled as a cascade of parallel linear and non-linear operations that can be connected recurrently, akin to a multi-layer, recurrent, artificial neural network. Given their tractable mathematical structure, we show that neuron models expressed in terms of parallel recurrent cascades can themselves be integrated into multi-layered artificial neural networks and trained to perform complex tasks. We go on to discuss potential implications and uses of these models for artificial intelligence. Overall, we argue that parallel, recurrent cascade models provide an important, unifying tool for capturing single-cell computation and exploring the algorithmic implications of physiological phenomena.
2021-07-05
Neuroscience (publié)
doi.org
Parallel and Recurrent Cascade Models as a Unifying Force for Understanding Subcellular Computation
Emerson F. Harkin
Peter R. Shen
Anisha Goel
Blake Richards
Richard Naud
Neurons are very complicated computational devices, incorporating numerous non-linear processes, particularly in their dendrites. Biophysica… (voir plus)l models capture these processes directly by explicitly modelling physiological variables, such as ion channels, current flow, membrane capacitance, etc. However, another option for capturing the complexities of real neural computation is to use cascade models, which treat individual neurons as a cascade of linear and non-linear operations, akin to a multi-layer artificial neural network. Recent research has shown that cascade models can capture single-cell computation well, but there are still a number of sub-cellular, regenerative dendritic phenomena that they cannot capture, such as the interaction between sodium, calcium, and NMDA spikes in different compartments. Here, we propose that it is possible to capture these additional phenomena using parallel, recurrent cascade models, wherein an individual neuron is modelled as a cascade of parallel linear and non-linear operations that can be connected recurrently, akin to a multi-layer, recurrent, artificial neural network. Given their tractable mathematical structure, we show that neuron models expressed in terms of parallel recurrent cascades can themselves be integrated into multi-layered artificial neural networks and trained to perform complex tasks. We go on to discuss potential implications and uses of these models for artificial intelligence. Overall, we argue that parallel, recurrent cascade models provide an important, unifying tool for capturing single-cell computation and exploring the algorithmic implications of physiological phenomena.
2021-07-05
Neuroscience (published)
doi.org
Beyond variance reduction: Understanding the true impact of baselines on policy optimization
Wesley Chung
Valentin Thomas
Marlos C. Machado
Nicolas Le Roux
2021-07-01
Proceedings of the 38th International Conference on Machine Learning (publié)
proceedings.mlr.press
arxiv.org
Directional Graph Networks
Dominique Beaini
Saro Passaro
Vincent Létourneau
William Hamilton
Gabriele Corso
Pietro Lio
The lack of anisotropic kernels in graph neural networks (GNNs) strongly limits their expressiveness, contributing to well-known issues such… (voir plus) as over-smoothing. To overcome this limitation, we propose the first globally consistent anisotropic kernels for GNNs, allowing for graph convolutions that are defined according to topologicaly-derived directional flows. First, by defining a vector field in the graph, we develop a method of applying directional derivatives and smoothing by projecting node-specific messages into the field. Then, we propose the use of the Laplacian eigenvectors as such vector field. We show that the method generalizes CNNs on an
2021-07-01
Proceedings of the 38th International Conference on Machine Learning (publié)
proceedings.mlr.press
openreview.net
RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting
Soumyasundar Pal
Liheng Ma
Yingxue Zhang
Mark 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.
2021-07-01
Proceedings of the 38th International Conference on Machine Learning (publié)
proceedings.mlr.press
arxiv.org