Publications

Network Design for Ultra-fast Delivery Services via Probabilistic Envelope Constrained Programs
Xin Wang
Okan Arslan
Jean-François Cordeau
Erick Delage
2022-12-31
SSRN Electronic Journal (publié)
doi.org
NEURAL MANIFOLDS AND GRADIENT-BASED ADAPTATION IN NEURAL-INTERFACE TASKS
Alexandre Payeur
Amy L. Orsborn
Guillaume Lajoie
. Neural activity tends to reside on manifolds whose dimension is much lower than the dimension of the whole neural state space. Experiments… (voir plus) using brain-computer interfaces with microelectrode arrays implanted in the motor cortex of nonhuman primates tested the hypothesis that external perturbations should produce different adaptation strategies depending on how “aligned” the perturbation is with respect to a pre-existing intrinsic manifold. On the one hand, perturbations within the manifold (WM) evoked fast reassociations of existing patterns for rapid adaptation. On the other hand, perturbations outside the manifold (OM) triggered the slow emergence of new neural patterns underlying a much slower—and, without adequate training protocols, inconsistent or virtually impossible—adaptation. This suggests that the time scale and the overall difficulty of the brain to adapt depend fundamentally on the structure of neural activity. Here, we used a simplified static Gaussian model to show that gradient-descent learning could explain the differences between adaptation to WM and OM perturbations. For small learning rates, we found that the adaptation speeds were different but the model eventually adapted to both perturbations. Moreover, sufficiently large learning rates could entirely prohibit adaptation to OM perturbations while preserving adaptation to WM perturbations, in agreement with experiments. Adopting an incremental training protocol, as has been done in experiments, permitted a swift recovery of a full adaptation in the cases where OM perturbations were previously impossible to relearn. Finally, we also found that gradient descent was compatible with the reassociation mechanism on short adaptation time scales. Since gradient descent has many biologically plausible variants, our findings thus establish gradient-based learning as a plausible mechanism for adaptation under network-level constraints, with a central role for the learning rate.
2022-12-31
(publié)
www.semanticscholar.org
NEURAL NETWORK-BASED SOLVERS FOR PDES
M. Cameron
Ian G Goodfellow
Yoshua Bengio
(1) N (x; θ) = Ll+1 ○ σl ○Ll ○ σl−1 ○ . . . ○ σ1 ○L1. The symbol Lk denotes the k’s affine operator of the form Lk(x) = … (voir plus)Akx + bk, while σk denotes a nonlinear function called an activation function. The activation functions are chosen by the user. The matrices Ak and shift vectors (or bias vectors) bk are encoded into the argument θ: θ = {Ak, bk} l+1 k=1. The term training neural network means finding {Ak, bk} l+1 k=1 such that N (x; θ) satisfies certain conditions. These conditions are described by the loss function chosen by the user. For example, one might want the neural network to assume certain values fj at certain points xj , j = 1, . . . ,N . These points x are called the training data. In this case, a common choice of the loss function is the least squares error:
2022-12-31
(publié)
www.semanticscholar.org
Noisy Pairing and Partial Supervision for Stylized Opinion Summarization
Reinald Kim
Mirella Lapata. 2020
Un-611
David M. Krueger
Emmanuel Bengio
Maxinder S. Kan-620
Tegan Maharaj
Asja Fischer
Aaron Courville
Somnath Basu
Roy Chowdhury
Chao Zhao
Tanya Goyal
Junyi Jiacheng Xu
Jessy Li
Ivor W. Tsang
James T. Kwok
Neil Houlsby
Andrei Giurgiu
Stanisław Jastrzębski … (voir 22 de plus)
Bruna Morrone
Quentin de Laroussilhe
Mona Gesmundo
Attariyan Sylvain
Gelly
Thomas Wolf
Lysandre Debut
Julien Victor Sanh
Clement Chaumond
Anthony Delangue
Pier-339 Moi
Tim ric Cistac
R´emi Rault
Morgan Louf
Funtow-900 Joe
Sam Davison
Patrick Shleifer
Von Platen
Clara Ma
Yacine Jernite
Julien Plu
Canwen Xu
Opinion summarization research has primar-001 ily focused on generating summaries reflect-002 ing important opinions from customer reviews 0… (voir plus)03 without paying much attention to the writing 004 style. In this paper, we propose the stylized 005 opinion summarization task, which aims to 006 generate a summary of customer reviews in 007 the desired (e.g., professional) writing style. 008 To tackle the difficulty in collecting customer 009 and professional review pairs, we develop a 010 non-parallel training framework, Noisy Pair-011 ing and Partial Supervision ( NAPA ), which 012 trains a stylized opinion summarization sys-013 tem from non-parallel customer and profes-014 sional review sets. We create a benchmark P RO - 015 S UM by collecting customer and professional 016 reviews from Yelp and Michelin. Experimental 017 results on P RO S UM and FewSum demonstrate 018 that our non-parallel training framework con-019 sistently improves both automatic and human 020 evaluations, successfully building a stylized 021 opinion summarization model that can gener-022 ate professionally-written summaries from cus-023 tomer reviews. 024
2022-12-31
(publié)
www.semanticscholar.org
A Novel Deep Multi-head Attentive Vulnerable Line Detector
Miles Q. Li
Benjamin C. M. Fung
Ashita Diwan
2022-12-31
Procedia Computer Science (publié)
doi.org
An Online Newton’s Method for Time-Varying Linear Equality Constraints
Jean-Luc Lupien
Antoine Lesage-Landry
We consider online optimization problems with time-varying linear equality constraints. In this framework, an agent makes sequential decisio… (voir plus)ns using only prior information. At every round, the agent suffers an environment-determined loss and must satisfy time-varying constraints. Both the loss functions and the constraints can be chosen adversarially. We propose the Online Projected Equality-constrained Newton Method (OPEN-M) to tackle this family of problems. We obtain sublinear dynamic regret and constraint violation bounds for OPEN-M under mild conditions. Namely, smoothness of the loss function and boundedness of the inverse Hessian at the optimum are required, but not convexity. Finally, we show OPEN-M outperforms state-of-the-art online constrained optimization algorithms in a numerical network flow application.
2022-12-31
IEEE Control Systems Letters (publié)
doi.org
arxiv.org
Optimism and Adaptivity in Policy Optimization
Veronica Chelu
Tom Zahavy
Arthur Guez
Doina Precup
Sebastian Flennerhag
2022-12-31
arXiv.org (prépublication)
doi.org
Optimizing Fairness over Time with Homogeneous Workers (Short Paper).
Bart-jan Van Rossum
Ying Chen
Andrea Lodi
2022-12-31
ATMOS (publié)
doi.org
Patient experience or patient satisfaction? A systematic review of child- and family-reported experience measures in pediatric surgery.
Julia Ferreira
Prachikumari Patel
Elena Guadagno
Nikki Ow
Jo Wray
Sherif Emil
Dan Poenaru
2022-12-31
Journal of Pediatric Surgery (publié)
doi.org
Physics-Guided Adversarial Machine Learning for Aircraft Systems Simulation
Houssem Ben Braiek
Thomas Reid
Foutse Khomh
In the context of aircraft system performance assessment, deep learning technologies allow us to quickly infer models from experimental meas… (voir plus)urements, with less detailed system knowledge than usually required by physics-based modeling. However, this inexpensive model development also comes with new challenges regarding model trustworthiness. This article presents a novel approach, physics-guided adversarial machine learning (ML), which improves the confidence over the physics consistency of the model. The approach performs, first, a physics-guided adversarial testing phase to search for test inputs revealing behavioral system inconsistencies, while still falling within the range of foreseeable operational conditions. Then, it proceeds with a physics-informed adversarial training to teach the model the system-related physics domain foreknowledge through iteratively reducing the unwanted output deviations on the previously uncovered counterexamples. Empirical evaluation on two aircraft system performance models shows the effectiveness of our adversarial ML approach in exposing physical inconsistencies of both the models and in improving their propensity to be consistent with physics domain knowledge.
2022-12-31
IEEE Transactions on Reliability (publié)
doi.org
arxiv.org
PipeBERT: High-throughput BERT Inference for ARM Big.LITTLE Multi-core Processors.
Hung-Yang Chang
Seyyed Hasan Mozafari
Cheng Chen
James J. Clark
Brett H. Meyer
Warren J. Gross
2022-12-31
J. Signal Process. Syst. (publié)
doi.org
Predicting Time to and Average Quality of Future Offers for Kidney Transplant Candidates Declining a Current Deceased Donor Kidney Offer: A Retrospective Cohort Study
Jonathan Jalbert
Jean-Noel Weller
Pierre-Luc Boivin
Sylvain Lavigne
Mehdi Taobane
Mike Pieper
Andrea Lodi
Heloise Cardinal
By providing personalized quantitative estimates of time to and quality of future offers, our new approach can inform the shared decision-ma… (voir plus)king process between transplant candidates and physicians when a kidney offer from a deceased donor is made by an ODO.
2022-12-31
Canadian Journal of Kidney Health and Disease (publié)
doi.org