Publications

Improving Pathological Structure Segmentation via Transfer Learning Across Diseases

Barleen Kaur

Paul Lemaitre

Raghav Mehta

Nazanin Mohammadi Sepahvand

Doina Precup

Douglas Arnold

Tal Arbel

2019-10-12

Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data (publié)

doi.org

Early Prediction of Alzheimer's Disease Progression Using Variational Autoencoders

Sumana Basu

Konrad Wagstyl

Azar Zandifar

D. Collins

Adriana Romero

Doina Precup

2019-10-09

Lecture Notes in Computer Science (publié)

doi.org

InfoMask: Masked Variational Latent Representation to Localize Chest Disease

Saeid Asgari Taghanaki

Mohammad Havaei

Tess Berthier

Francis Dutil

Lisa Di Jorio

Ghassan Hamarneh

Yoshua Bengio

2019-10-09

Lecture Notes in Computer Science (publié)

doi.org

arxiv.org

Machine Learning Advantages in Canadian Astrophysics

Kim Venn

Sébastien Fabbro

Adrian Liu

Yashar Hezaveh

Laurence Perreault-Levasseur

Gwendolyn Eadie

Sara Ellison

Joanna Woo

JJ Kavelaars

Kwang Moo Yi

Renée Hložek

Jo Bovy

Hossen Teimoorinia

Siamak Ravanbakhsh

Locke Spencer

The application of machine learning (ML) methods to the analysis of astrophysical datasets is on the rise, particularly as the computing pow… (voir plus)er and complex algorithms become more powerful and accessible. As the field of ML enjoys a continuous stream of breakthroughs, its applications demonstrate the great potential of ML, ranging from achieving tens of millions of times increase in analysis speed (e.g., modeling of gravitational lenses or analysing spectroscopic surveys) to solutions of previously unsolved problems (e.g., foreground subtraction or efficient telescope operations). The number of astronomical publications that include ML has been steadily increasing since 2010.
With the advent of extremely large datasets from a new generation of surveys in the 2020s, ML methods will become an indispensable tool in astrophysics. Canada is an unambiguous world leader in the development of the field of machine learning, attracting large investments and skilled researchers to its prestigious AI Research Institutions. This provides a unique opportunity for Canada to also be a world leader in the application of machine learning in the field of astrophysics, and foster the training of a new generation of highly skilled researchers.

2019-10-01

ArXiv (prépublication)

doi.org

arxiv.org

Reinforcement Learning Models of Human Behavior: Reward Processing in Mental Disorders

Baihan Lin

Guillermo Cecchi

Djallel Bouneffouf

Jenna Reinen

Irina Rish

Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for a reinforcement … (voir plus)learning problem, which extends the standard Q-learning approach to incorporate a two-stream framework of reward processing with biases biologically associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain. For the AI community, the development of agents that react differently to different types of rewards can enable us to understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems. Empirically, the proposed model outperforms Q-Learning and Double Q-Learning in artificial scenarios with certain reward distributions and real-world human decision making gambling tasks. Moreover, from the behavioral modeling perspective, our parametric framework can be viewed as a first step towards a unifying computational model capturing reward processing abnormalities across multiple mental conditions and user preferences in long-term recommendation systems.

2019-10-01

NeurIPS.cc/2019/Workshop/Neuro_AI (poster)

openreview.net

Augmenting learning using symmetry in a biologically-inspired domain

Shruti Mishra

Abbas Abdolmaleki

Arthur Guez

Piotr Trochim

Doina Precup

Invariances to translation, rotation and other spatial transformations are a hallmark of the laws of motion, and have widespread use in the … (voir plus)natural sciences to reduce the dimensionality of systems of equations. In supervised learning, such as in image classification tasks, rotation, translation and scale invariances are used to augment training datasets. In this work, we use data augmentation in a similar way, exploiting symmetry in the quadruped domain of the DeepMind control suite (Tassa et al. 2018) to add to the trajectories experienced by the actor in the actor-critic algorithm of Abdolmaleki et al. (2018). In a data-limited regime, the agent using a set of experiences augmented through symmetry is able to learn faster. Our approach can be used to inject knowledge of invariances in the domain and task to augment learning in robots, and more generally, to speed up learning in realistic robotics applications.

2019-09-30

ArXiv (prépublication)

arxiv.org

Depth with Nonlinearity Creates No Bad Local Minima in ResNets

Kenji Kawaguchi

Yoshua Bengio

2019-09-30

Neural Networks (publié)

doi.org

arxiv.org

Evaluation of a web-based tool for labelling potential hospital outbreaks: a mixed methods study

B. Leclère

David L Buckeridge

D. Lepelletier

2019-09-30

Journal of Hospital Infection (publié)

doi.org

Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localization

Md Mahfuzur Rahman Siddiquee

Zongwei Zhou

Nima Tajbakhsh

Ruibin Feng

Michael B. Gotway

Yoshua Bengio

Jianming Liang

Generative adversarial networks (GANs) have ushered in a revolution in image-to-image translation. The development and proliferation of GANs… (voir plus) raises an interesting question: can we train a GAN to remove an object, if present, from an image while otherwise preserving the image? Specifically, can a GAN "virtually heal" anyone by turning his medical image, with an unknown health status (diseased or healthy), into a healthy one, so that diseased regions could be revealed by subtracting those two images? Such a task requires a GAN to identify a minimal subset of target pixels for domain translation, an ability that we call fixed-point translation, which no GAN is equipped with yet. Therefore, we propose a new GAN, called Fixed-Point GAN, trained by (1) supervising same-domain translation through a conditional identity loss, and (2) regularizing cross-domain translation through revised adversarial, domain classification, and cycle consistency loss. Based on fixed-point translation, we further derive a novel framework for disease detection and localization using only image-level annotation. Qualitative and quantitative evaluations demonstrate that the proposed method outperforms the state of the art in multi-domain image-to-image translation and that it surpasses predominant weakly-supervised localization methods in both disease detection and localization. Implementation is available at https://github.com/jlianglab/Fixed-Point-GAN.

2019-09-30

2019 IEEE/CVF International Conference on Computer Vision (ICCV) (publié)

doi.org

arxiv.org

Patterns of autism symptoms: hidden structure in the ADOS and ADI-R instruments

Jeremy Lefort-Besnard

Kai Vogeley

Leonhard Schilbach

Gael Varoquaux

Bertrand Thirion

Guillaume Dumas

Danilo Bzdok