Publications

Predicting ice flow using machine learning
Yimeng Min
S. Karthik Mukkavilli
Yoshua Bengio
Though machine learning has achieved notable success in modeling sequential and spatial data for speech recognition and in computer vision, … (voir plus)applications to remote sensing and climate science problems are seldom considered. In this paper, we demonstrate techniques from unsupervised learning of future video frame prediction, to increase the accuracy of ice flow tracking in multi-spectral satellite images. As the volume of cryosphere data increases in coming years, this is an interesting and important opportunity for machine learning to address a global challenge for climate change, risk management from floods, and conserving freshwater resources. Future frame prediction of ice melt and tracking the optical flow of ice dynamics presents modeling difficulties, due to uncertainties in global temperature increase, changing precipitation patterns, occlusion from cloud cover, rapid melting and glacier retreat due to black carbon aerosol deposition, from wildfires or human fossil emissions. We show the adversarial learning method helps improve the accuracy of tracking the optical flow of ice dynamics compared to existing methods in climate science. We present a dataset, IceNet, to encourage machine learning research and to help facilitate further applications in the areas of cryospheric science and climate change.
2019-10-19
ArXiv (prépublication)
arxiv.org
arxiv.org
A language processing algorithm for predicting tactical solutions to an operational planning problem under uncertainty
Emma Frejinger
Eric Larsen
This paper is devoted to the prediction of solutions to a stochastic discrete optimization problem. Through an application, we illustrate ho… (voir plus)w we can use a state-of-the-art neural machine translation (NMT) algorithm to predict the solutions by defining appropriate vocabularies, syntaxes and constraints. We attend to applications where the predictions need to be computed in very short computing time -- in the order of milliseconds or less. The results show that with minimal adaptations to the model architecture and hyperparameter tuning, the NMT algorithm can produce accurate solutions within the computing time budget. While these predictions are slightly less accurate than approximate stochastic programming solutions (sample average approximation), they can be computed faster and with less variability.
2019-10-17
ArXiv (prépublication)
arxiv.org
arxiv.org
Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference
Raghav Mehta
Thomas Christinck
Tanya Nair
Aurélie Bussy
Paul Lemaitre
Swapna Premasiri
Manuela Costantino
Mallar Chakravarty
Douglas Arnold
Yarin Gal
Tal Arbel
2019-10-16
UNSURE/CLIP@MICCAI (publié)
doi.org
Saliency Based Deep Neural Network for Automatic Detection of Gadolinium-Enhancing Multiple Sclerosis Lesions in Brain MRI
Joshua D. Durso-Finley
Douglas Arnold
Tal Arbel
2019-10-16
BrainLes@MICCAI (publié)
doi.org
SGP: Spotting Groups Polluting the Online Political Discourse
Junhao Wang
Sacha Lévy
Ren Wang
Aayushi Kulshrestha
Reihaneh Rabbany
Social media sites are becoming a key factor in politics. These platforms are easy to manipulate for the purpose of distorting information s… (voir plus)pace to confuse and distract voters. It is of paramount importance for social media platforms, users engaged with online political discussions, as well as government agencies to understand the dynamics on social media, and identify malicious groups engaging in misinformation campaigns and thus polluting the general discourse around a topic of interest. Past works to identify such disruptive patterns are mostly focused on analyzing user-generated content such as tweets. In this study, we take a holistic approach and propose SGP to provide an informative birds eye view of all the activities in these social media sites around a broad topic and detect coordinated groups suspicious of engaging in misinformation campaigns. To show the effectiveness of SGP, we deploy it to provide a concise overview of polluting activity on Twitter around the upcoming 2019 Canadian Federal Elections, by analyzing over 60 thousand user accounts connected through 3.4 million connections and 1.3 million hashtags. Users in the polluting groups detected by SGP-flag are over 4x more likely to become suspended while majority of these highly suspicious users detected by SGP-flag escaped Twitter's suspending algorithm. Moreover, while few of the polluting hashtags detected are linked to misinformation campaigns, SGP-sig also flags others that have not been picked up on. More importantly, we also show that a large coordinated set of right-winged conservative groups based in the US are heavily engaged in Canadian politics.
2019-10-15
ArXiv (prépublication)
arxiv.org
arxiv.org
Actor Critic with Differentially Private Critic
Jonathan Lebensold
William Hamilton
Borja Balle
Doina Precup
Reinforcement learning algorithms are known to be sample inefficient, and often performance on one task can be substantially improved by lev… (voir plus)eraging information (e.g., via pre-training) on other related tasks. In this work, we propose a technique to achieve such knowledge transfer in cases where agent trajectories contain sensitive or private information, such as in the healthcare domain. Our approach leverages a differentially private policy evaluation algorithm to initialize an actor-critic model and improve the effectiveness of learning in downstream tasks. We empirically show this technique increases sample efficiency in resource-constrained control problems while preserving the privacy of trajectories collected in an upstream task.
2019-10-13
ArXiv (prépublication)
doi.org
arxiv.org
Deep learning for Aerosol Forecasting
Caleb Hoyne
S. Karthik Mukkavilli
David Meger
Reanalysis datasets combining numerical physics models and limited observations to generate a synthesised estimate of variables in an Earth … (voir plus)system, are prone to biases against ground truth. Biases identified with the NASA Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) aerosol optical depth (AOD) dataset, against the Aerosol Robotic Network (AERONET) ground measurements in previous studies, motivated the development of a deep learning based AOD prediction model globally. This study combines a convolutional neural network (CNN) with MERRA-2, tested against all AERONET sites. The new hybrid CNN-based model provides better estimates validated versus AERONET ground truth, than only using MERRA-2 reanalysis.
2019-10-13
ArXiv (prépublication)
arxiv.org
arxiv.org
Nash Games Among Stackelberg Leaders
Margarida Carvalho
Gabriele Dragotto
Felipe Feijoo
Andrea Lodi
Sriram Sankaranarayanan
We analyze Nash games played among leaders of Stackelberg games (NASP). We show it is Σ p 2 - hard to decide if the game has a mixed-strate… (voir plus)gy Nash equilibrium (MNE), even when there are only two leaders and each leader has one follower. We provide a finite time algorithm with a running time bounded by O (2 2 n ) which computes MNEs for NASP when it exists and returns infeasibility if no MNE exists. We also provide two ways to improve the algorithm which involves constructing a series of inner approximations (alternatively, outer approximations) to the leaders’ feasible region that will provably obtain the required MNE. Finally, we test our algorithms on a range of NASPs arising out of a game in the energy market, where countries act as Stackelberg leaders who play a Nash game, and the domestic producers act as the followers.
2019-10-13
arXiv.org (prépublication)
dblp.uni-trier.de
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