Publications

Image-to-image Mapping with Many Domains by Sparse Attribute Transfer

Matthew Amodio

2020-06-22

arXiv (prépublication)

Rethinking Distributional Matching Based Domain Adaptation

Bin Li

Yezhen Wang

Tong Che

Shanghang Zhang

Sicheng Zhao

Pengfei Xu

Wenzhen Zhou

Kurt W. Keutzer

Domain adaptation (DA) is a technique that transfers predictive models trained on a labeled source domain to an unlabeled target domain, wit… (voir plus)h the core difficulty of resolving distributional shift between domains. Currently, most popular DA algorithms are based on distributional matching (DM). However in practice, realistic domain shifts (RDS) may violate their basic assumptions and as a result these methods will fail. In this paper, in order to devise robust DA algorithms, we first systematically analyze the limitations of DM based methods, and then build new benchmarks with more realistic domain shifts to evaluate the well-accepted DM methods. We further propose InstaPBM, a novel Instance-based Predictive Behavior Matching method for robust DA. Extensive experiments on both conventional and RDS benchmarks demonstrate both the limitations of DM methods and the efficacy of InstaPBM: Compared with the best baselines, InstaPBM improves the classification accuracy respectively by

2020-06-22

ArXiv (prépublication)

HNHN: Hypergraph Networks with Hyperedge Neurons

Yihe Dong

W. Sawin

2020-06-21

ArXiv (prépublication)

Learning to Prove from Synthetic Theorems

Eser Aygün

Vlad Firoiu

Laurent Orseau

Shibl Mourad

A major challenge in applying machine learning to automated theorem proving is the scarcity of training data, which is a key ingredient in t… (voir plus)raining successful deep learning models. To tackle this problem, we propose an approach that relies on training with synthetic theorems, generated from a set of axioms. We show that such theorems can be used to train an automated prover and that the learned prover transfers successfully to human-generated theorems. We demonstrate that a prover trained exclusively on synthetic theorems can solve a substantial fraction of problems in TPTP, a benchmark dataset that is used to compare state-of-the-art heuristic provers. Our approach outperforms a model trained on human-generated problems in most axiom sets, thereby showing the promise of using synthetic data for this task.

2020-06-18

ArXiv (prépublication)

Individual differences in interpersonal coordination

Julia Ayache

Guillaume Dumas

A. Sumich

D. Kuss

Darren Rhodes

Nadja Heym

2020-06-17

(publié)

Special Issue on Novel Informatics Approaches to COVID-19 Research

Huanan Xu

David L Buckeridge

Fei Wang Guest Editors

2020-06-16

Journal of Biomedical Informatics (publié)

Quantized Guided Pruning for Efficient Hardware Implementations of Deep Neural Networks

Ghouthi Boukli hacene

Vincent Gripon

Matthieu Arzel

Nicolas Farrugia

Deep Neural Networks (DNNs) in general and Convolutional Neural Networks (CNNs) in particular are state-of-the-art in numerous computer visi… (voir plus)on tasks such as object classification and detection. However, the large amount of parameters they contain leads to a high computational complexity and strongly limits their usability in budget-constrained devices such as embedded devices. In this paper, we propose a combination of a pruning technique and a quantization scheme that effectively reduce the complexity and memory usage of convolutional layers of CNNs, by replacing the complex convolutional operation by a low-cost multiplexer. We perform experiments on CIFAR10, CIFAR100 and SVHN datasets and show that the proposed method achieves almost state-of-the-art accuracy, while drastically reducing the computational and memory footprints compared to the baselines. We also propose an efficient hardware architecture, implemented on Field Programmable Gate Arrays (FPGAs), to accelerate inference, which works as a pipeline and accommodates multiple layers working at the same time to speed up the inference process. In contrast with most proposed approaches which have used external memory or software defined memory controllers, our work is based on algorithmic optimization and full-hardware design, enabling a direct, on-chip memory implementation of a DNN while keeping close to state of the art accuracy.

2020-06-15

2020 18th IEEE International New Circuits and Systems Conference (NEWCAS) (publié)

Untangling tradeoffs between recurrence and self-attention in neural networks

2020-06-15

arXiv (prépublication)

User-Centered Design for Promoting Patient Engagement in Chronic Diseases Management: The Development of CONCERTO+

Marie-Pierre Gagnon

Mame Ndiaye

Alain Larouche

Guylaine Chabot

Christian Chabot

Ronald Buyl

Jean-Paul Fortin

Anik Giguère

Annie LeBlanc

France Légaré

Aude Motulsky

Claude Sicotte

Holly O Witteman

Eric Kavanagh

Frédéric Lépinay

Jacynthe Roberge

Hina Hakim

Myriam Brunet-Gauthier

Carole Délétroz

S. A. Rahimi … (voir 2 de plus)

Jack Tchuente

Maxime Sasseville

Multimorbidity increases care needs among people with chronic diseases. In order to support communication between patients, their informal c… (voir plus)aregivers and their healthcare teams, we developed CONCERTO+, a patient portal for chronic disease management in primary care. A user-centered design comprising 3 iterations with patients and informal caregivers was performed. Clinicians were also invited to provide feedback on the feasibility of the solution. Several improvements were brought to CONCERTO+, and it is now ready to be implemented in real-life setting.

2020-06-15

Medical Informatics Europe (publié)

Learning Causal Models Online

Khurram Javed

Martha White

2020-06-11

ArXiv (prépublication)

Reza Babanezhad Harikandeh

To Each Optimizer a Norm, To Each Norm its Generalization

Sharan Vaswani

Jose Gallego

Aaron Mishkin

Simon Lacoste-Julien

Nicolas Roux

We study the implicit regularization of optimization methods for linear models interpolating the training data in the under-parametrized and… (voir plus) over-parametrized regimes. Since it is difficult to determine whether an optimizer converges to solutions that minimize a known norm, we flip the problem and investigate what is the corresponding norm minimized by an interpolating solution. Using this reasoning, we prove that for over-parameterized linear regression, projections onto linear spans can be used to move between different interpolating solutions. For under-parameterized linear classification, we prove that for any linear classifier separating the data, there exists a family of quadratic norms ||.||_P such that the classifier's direction is the same as that of the maximum P-margin solution. For linear classification, we argue that analyzing convergence to the standard maximum l2-margin is arbitrary and show that minimizing the norm induced by the data results in better generalization. Furthermore, for over-parameterized linear classification, projections onto the data-span enable us to use techniques from the under-parameterized setting. On the empirical side, we propose techniques to bias optimizers towards better generalizing solutions, improving their test performance. We validate our theoretical results via synthetic experiments, and use the neural tangent kernel to handle non-linear models.

2020-06-10

ArXiv (prépublication)