Publications

Deep discriminant analysis for task-dependent compact network search
Qing Tian
Tal Arbel
James J. Clark
Most of today's popular deep architectures are hand-engineered for general purpose applications. However, this design procedure usually lead… (see more)s to massive redundant, useless, or even harmful features for specific tasks. Such unnecessarily high complexities render deep nets impractical for many real-world applications, especially those without powerful GPU support. In this paper, we attempt to derive task-dependent compact models from a deep discriminant analysis perspective. We propose an iterative and proactive approach for classification tasks which alternates between (1) a pushing step, with an objective to simultaneously maximize class separation, penalize co-variances, and push deep discriminants into alignment with a compact set of neurons, and (2) a pruning step, which discards less useful or even interfering neurons. Deconvolution is adopted to reverse `unimportant' filters' effects and recover useful contributing sources. A simple network growing strategy based on the basic Inception module is proposed for challenging tasks requiring larger capacity than what the base net can offer. Experiments on the MNIST, CIFAR10, and ImageNet datasets demonstrate our approach's efficacy. On ImageNet, by pushing and pruning our grown Inception-88 model, we achieve better-performing models than smaller deep Inception nets grown, residual nets, and famous compact nets at similar sizes. We also show that our grown deep Inception nets (without hard-coded dimension alignment) can beat residual nets of similar complexities.
2020-09-29
arXiv.org (preprint)
dblp.uni-trier.de
Learning to Summarize Long Texts with Memory Compression and Transfer
Jaehong Park
Jonathan Pilault
Chris Pal
2020-09-28
ArXiv (preprint)
arxiv.org
arxiv.org
Optimal Control of Network-Coupled Subsystems: Spectral Decomposition and Low-Dimensional Solutions
Shuang Gao
Aditya Mahajan
In this article, we investigate the optimal control of network-coupled subsystems with coupled dynamics and costs. The dynamics coupling may… (see more) be represented by the adjacency matrix, the Laplacian matrix, or any other symmetric matrix corresponding to an underlying weighted undirected graph. Cost couplings are represented by two coupling matrices which have the same eigenvectors as the coupling matrix in the dynamics. We use the spectral decomposition of these three coupling matrices to decompose the overall system into
2020-09-25
ArXiv (preprint)
doi.org
arxiv.org
Generating Multiscale Amorphous Molecular Structures Using Deep Learning: A Study in 2D.
Michael Kilgour
Nicolas Gastellu
David Y. T. Hui
Yoshua Bengio
Lena Simine
Amorphous molecular assemblies appear in a vast array of systems: from living cells to chemical plants and from everyday items to new device… (see more)s. The absence of long-range order in amorphous materials implies that precise knowledge of their underlying structures throughout is needed to rationalize and control their properties at the mesoscale. Standard computational simulations suffer from exponentially unfavorable scaling of the required compute with system size. We present a method based on deep learning that leverages the finite range of structural correlations for an autoregressive generation of disordered molecular aggregates up to arbitrary size from small-scale computational or experimental samples. We benchmark performance on self-assembled nanoparticle aggregates and proceed to simulate monolayer amorphous carbon with atomistic resolution. This method bridges the gap between the nanoscale and mesoscale simulations of amorphous molecular systems.
2020-09-24
Journal of Physical Chemistry Letters (published)
doi.org
Preface
Tal Arbel
Ismail Ben Ayed
Marleen de Bruijne
Maxime Descoteaux
Hervé Lombaert
Chris Pal
2020-09-21
Proceedings of the Third Conference on Medical Imaging with Deep Learning (published)
proceedings.mlr.press
A learning-based algorithm to quickly compute good primal solutions for Stochastic Integer Programs
Yoshua Bengio
Emma Frejinger
Andrea Lodi
Rahul Anuj Patel
Sriram Sankaranarayanan
2020-09-19
Integration of Constraint Programming, Artificial Intelligence, and Operations Research (published)
doi.org
arxiv.org
Practical Dynamic SC-Flip Polar Decoders: Algorithm and Implementation
Furkan Ercan
Thibaud Tonnellier
Nghia Doan
Warren Gross
SC-Flip (SCF) is a low-complexity polar code decoding algorithm with improved performance, and is an alternative to high-complexity (CRC)-ai… (see more)ded SC-List (CA-SCL) decoding. However, the performance improvement of SCF is limited since it can correct up to only one channel error (
2020-09-17
ArXiv (preprint)
doi.org
arxiv.org
A normative modelling approach reveals age-atypical cortical thickness in a subgroup of males with autism spectrum disorder
Richard A.I. Bethlehem
Jakob Seidlitz
Rafael Romero-Garcia
Stavros Trakoshis
Guillaume Dumas
Michael V. Lombardo
2020-09-04
Communications Biology (published)
doi.org
Keynote Lecture - Building Knowledge For AI AgentsWith Reinforcement Learning
Doina Precup
Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings.… (see more) Reinforcement learning allows autonomous agents to learn how to act in a stochastic, unknown environment, with which they can interact. Deep reinforcement learning, in particular, has achieved great success in well-defined application domains, such as Go or chess, in which an agent has to learn how to act and there is a clear success criterion. In this talk, I will focus on the potential role of reinforcement learning as a tool for building knowledge representations in AI agents whose goal is to perform continual learning. I will examine a key concept in reinforcement learning, the value function, and discuss its generalization to support various forms of predictive knowledge. I will also discuss the role of temporally extended actions, and their associated predictive models, in learning procedural knowledge. In order to tame the possible complexity of learning knowledge representations, reinforcement learning agents can use the concepts of intents (ie intended consequences of courses of actions) and affordances (which capture knowlege about where actions can be applied). Finally, I will discuss the challenge of how to evaluate reinforcement learning agents whose goal is not just to control their environment, but also to build knowledge about their world.
2020-09-03
International Conference on Computational Photography (published)
doi.org
Shared Decision Making in Surgery: A Meta-Analysis of Existing Literature
Kacper Niburski
Elena Guadagno
Samira Abbasgholizadeh-Rahimi
Dan Poenaru
2020-09-03
The Patient - Patient-Centered Outcomes Research (published)
doi.org
Correction to: Why public health matters today and tomorrow: the role of applied public health research
Lindsay McLaren
Paula Braitstein
David Buckeridge
Damien Contandriopoulos
Maria I. Creatore
Guy Faulkner
David Hammond
Steven J. Hoffman
Yan Kestens
Scott Leatherdale
Jonathan McGavock
Wendy V. Norman
Candace Nykiforuk
Valéry Ridde
Janet Smylie
The article “Why public health matters today and tomorrow: the role of applied public health research,” written by Lindsay McLaren et al… (see more)., was originally published Online First without Open Access.
2020-09-02
Canadian Journal of Public Health = Revue Canadienne de Santé Publique (published)
doi.org
Traceability Network Analysis: A Case Study of Links in Issue Tracking Systems
Alexander Nicholson
Deeksha M. Arya
Jin Guo
Traceability links between software artifacts serve as an invaluable resource for reasoning about software products and their development pr… (see more)ocess. Most conventional methods for capturing traceability are based on pair-wise artifact relations such as trace matrices or navigable links between two directly related artifacts. However, this limited view of trace links ignores the propagating effect of artifact connections as well as the trace link properties at a project level. In this work, we propose the use of network structures to provide another perspective from which reasoning on a collective of trace events is possible. We explore various network analysis techniques in the issue tracking system of sixty-six open source projects. Our observation reveals two salient properties of the traceability network, i.e. scale free and triadic closure. These properties provide a strong indication of the applicability of network analysis tools and can be used to identify and examine important "hub" issues. As a stepping stone, these properties can further support project status analysis and link type prediction. As a proof-of-concept, we demonstrate the effectiveness of applying the triadic closure property to link type prediction.
2020-09-01
International Workshop on Artificial Intelligence for Requirements Engineering (published)
doi.org