Publications

The Cost of Untracked Diversity in Brain-Imaging Prediction
Oualid Benkarim
Casey Paquola
Bo-yong Park
Valeria Kebets
Seok-Jun Hong
Reinder Vos de Wael
Shaoshi Zhang
B.T. Thomas Yeo
Michael Eickenberg
Tian Ge
Jean-Baptiste Poline
Boris Bernhardt
Danilo Bzdok
Brain-imaging research enjoys increasing adoption of supervised machine learning for singlesubject disease classification. Yet, the success … (voir plus)of these algorithms likely depends on population diversity, including demographic differences and other factors that may be outside of primary scientific interest. Here, we capitalize on propensity scores as a composite confound index to quantify diversity due to major sources of population stratification. We delineate the impact of population heterogeneity on the predictive accuracy and pattern stability in two separate clinical cohorts: the Autism Brain Imaging Data Exchange (ABIDE, n=297) and the Healthy Brain Network (HBN, n=551). Across various analysis scenarios, our results uncover the extent to which cross-validated prediction performances are interlocked with diversity. The instability of extracted brain patterns attributable to diversity is located preferentially to the default mode network. Our collective findings highlight the limitations of prevailing deconfounding practices in mitigating the full consequences of population diversity.
2021-06-16
bioRxiv (prépublication)
doi.org
Improving Continuous Normalizing Flows using a Multi-Resolution Framework
Vikram Voleti
Chris Finlay
Adam Oberman
Christopher Pal
Recent work has shown that Continuous Normalizing Flows (CNFs) can serve as generative models of images with exact likelihood calculation an… (voir plus)d invertible generation/density estimation. In this work we introduce a Multi-Resolution variant of such models (MRCNF). We introduce a transformation between resolutions that allows for no change in the log likelihood. We show that this approach yields comparable likelihood values for various image datasets, with improved performance at higher resolutions, with fewer parameters, using only 1 GPU.
2021-06-14
ICML.cc/2021/Workshop/INNF (poster)
openreview.net
openreview.net
Randomized Exploration for Reinforcement Learning with General Value Function Approximation
Haque Ishfaq
Qiwen Cui
Viet Nguyen
Alex Ayoub
Zhuoran Yang
Zhaoran Wang
Doina Precup
Lin F. Yang
2021-06-14
ArXiv (prépublication)
proceedings.mlr.press
proceedings.mlr.press
Variational Causal Networks: Approximate Bayesian Inference over Causal Structures
Yashas Annadani
Jonas Rothfuss
Alexandre Lacoste
Nino Scherrer
Anirudh Goyal
Yoshua Bengio
Stefan Bauer
Learning the causal structure that underlies data is a crucial step towards robust real-world decision making. The majority of existing work… (voir plus) in causal inference focuses on determining a single directed acyclic graph (DAG) or a Markov equivalence class thereof. However, a crucial aspect to acting intelligently upon the knowledge about causal structure which has been inferred from finite data demands reasoning about its uncertainty. For instance, planning interventions to find out more about the causal mechanisms that govern our data requires quantifying epistemic uncertainty over DAGs. While Bayesian causal inference allows to do so, the posterior over DAGs becomes intractable even for a small number of variables. Aiming to overcome this issue, we propose a form of variational inference over the graphs of Structural Causal Models (SCMs). To this end, we introduce a parametric variational family modelled by an autoregressive distribution over the space of discrete DAGs. Its number of parameters does not grow exponentially with the number of variables and can be tractably learned by maximising an Evidence Lower Bound (ELBO). In our experiments, we demonstrate that the proposed variational posterior is able to provide a good approximation of the true posterior.
2021-06-13
ArXiv (prépublication)
arxiv.org
arxiv.org
Comparative Study of Learning Outcomes for Online Learning Platforms
Francois St-Hilaire
Nathan J. Burns
Robert Belfer
Muhammad Shayan
Ariella Smofsky
Dung D. Vu
Antoine Frau
Joseph Potochny
Farid Faraji
Vincent Pavero
Neroli Ko
Ansona Onyi Ching
Sabina Elkins
A. Stepanyan
Adela Matajova
Laurent Charlin
Yoshua Bengio
Iulian V. Serban
Ekaterina Kochmar
2021-06-11
Lecture Notes in Computer Science (publié)
doi.org
arxiv.org
Understanding Capacity Saturation in Incremental Learning
Shenyang Huang
Vincent François-Lavet
Guillaume Rabusseau
2021-06-07
Canadian Conference on AI (publié)
doi.org
Learning Brain Dynamics With Coupled Low-Dimensional Nonlinear Oscillators and Deep Recurrent Networks.
Germán Abrevaya
Guillaume Dumas
Aleksandr Y. Aravkin
Peng Zheng
Jean-christophe Gagnon-audet
James R. Kozloski
Pablo Polosecki
Guillaume Lajoie
David D. Cox
Silvina Ponce Dawson
Guillermo A. Cecchi
Irina Rish
Many natural systems, especially biological ones, exhibit complex multivariate nonlinear dynamical behaviors that can be hard to capture by … (voir plus)linear autoregressive models. On the other hand, generic nonlinear models such as deep recurrent neural networks often require large amounts of training data, not always available in domains such as brain imaging; also, they often lack interpretability. Domain knowledge about the types of dynamics typically observed in such systems, such as a certain type of dynamical systems models, could complement purely data-driven techniques by providing a good prior. In this work, we consider a class of ordinary differential equation (ODE) models known as van der Pol (VDP) oscil lators and evaluate their ability to capture a low-dimensional representation of neural activity measured by different brain imaging modalities, such as calcium imaging (CaI) and fMRI, in different living organisms: larval zebrafish, rat, and human. We develop a novel and efficient approach to the nontrivial problem of parameters estimation for a network of coupled dynamical systems from multivariate data and demonstrate that the resulting VDP models are both accurate and interpretable, as VDP's coupling matrix reveals anatomically meaningful excitatory and inhibitory interactions across different brain subsystems. VDP outperforms linear autoregressive models (VAR) in terms of both the data fit accuracy and the quality of insight provided by the coupling matrices and often tends to generalize better to unseen data when predicting future brain activity, being comparable to and sometimes better than the recurrent neural networks (LSTMs). Finally, we demonstrate that our (generative) VDP model can also serve as a data-augmentation tool leading to marked improvements in predictive accuracy of recurrent neural networks. Thus, our work contributes to both basic and applied dimensions of neuroimaging: gaining scientific insights and improving brain-based predictive models, an area of potentially high practical importance in clinical diagnosis and neurotechnology.
2021-06-06
Neural Computation (inconnu)
doi.org
CMIM: Cross-Modal Information Maximization For Medical Imaging
Tristan Sylvain
Francis Dutil
Tess Berthier
Lisa Di Jorio
Margaux Luck
R Devon Hjelm
Yoshua Bengio
In hospitals, data are siloed to specific information systems that make the same information available under different modalities such as th… (voir plus)e different medical imaging exams the patient undergoes (CT scans, MRI, PET, Ultrasound, etc.) and their associated radiology reports. This offers unique opportunities to obtain and use at train-time those multiple views of the same information that might not always be available at test-time.In this paper, we propose an innovative framework that makes the most of available data by learning good representations of a multi-modal input that are resilient to modality dropping at test-time, using recent advances in mutual information maximization. By maximizing cross-modal information at train time, we are able to outperform several state-of-the-art baselines in two different settings, medical image classification, and segmentation. In particular, our method is shown to have a strong impact on the inference-time performance of weaker modalities.
2021-06-05
IEEE International Conference on Acoustics, Speech, and Signal Processing (publié)
doi.org
Double-Linear Thompson Sampling for Context-Attentive Bandits
Djallel Bouneffouf
Raphael Feraud
Sohini Upadhyay
Yasaman Khazaeni
Irina Rish
In this paper, we analyze and extend an online learning frame-work known as Context-Attentive Bandit, motivated by various practical applica… (voir plus)tions, from medical diagnosis to dialog systems, where due to observation costs only a small subset of a potentially large number of context variables can be observed at each iteration; however, the agent has a freedom to choose which variables to observe. We derive a novel algorithm, called Context-Attentive Thompson Sampling (CATS), which builds upon the Linear Thompson Sampling approach, adapting it to Context-Attentive Bandit setting. We provide a theoretical regret analysis and an extensive empirical evaluation demonstrating advantages of the proposed approach over several baseline methods on a variety of real-life datasets.
2021-06-05
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (publié)
doi.org
arxiv.org
Toward Skills Dialog Orchestration with Online Learning
Djallel Bouneffouf
Raphael Feraud
Sohini Upadhyay
Mayank Agarwal
Yasaman Khazaeni
Irina Rish
Building multi-domain AI agents is a challenging task and an open problem in the area of AI. Within the domain of dialog, the ability to orc… (voir plus)hestrate multiple independently trained dialog agents, or skills, to create a unified system is of particular significance. In this work, we study the task of online posterior dialog orchestration, where we define posterior orchestration as the task of selecting a subset of skills which most appropriately answer a user input using features extracted from both the user input and the individual skills. To account for the various costs associated with extracting skill features, we consider online posterior orchestration under a skill execution budget. We formalize this setting as Context Attentive Bandit with Observations (CABO), a variant of context attentive bandits, and evaluate it on proprietary conversational datasets.
2021-06-05
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (publié)
doi.org
Multimodal dynamics modeling for off-road autonomous vehicles
Jean-François Tremblay
Travis Manderson
Aurélio Noca
Gregory Dudek
David Meger
Dynamics modeling in outdoor and unstructured environments is difficult because different elements in the environment interact with the robo… (voir plus)t in ways that can be hard to predict. Leveraging multiple sensors to perceive maximal information about the robot's environment is thus crucial when building a model to perform predictions about the robot's dynamics with the goal of doing motion planning. We design a model capable of long-horizon motion predictions, leveraging vision, lidar and proprioception, which is robust to arbitrarily missing modalities at test time. We demonstrate in simulation that our model is able to leverage vision to predict traction changes. We then test our model using a real-world challenging dataset of a robot navigating through a forest, performing predictions in trajectories unseen during training. We try different modality combinations at test time and show that, while our model performs best when all modalities are present, it is still able to perform better than the baseline even when receiving only raw vision input and no proprioception, as well as when only receiving proprioception. Overall, our study demonstrates the importance of leveraging multiple sensors when doing dynamics modeling in outdoor conditions.
2021-06-04
2021 IEEE International Conference on Robotics and Automation (ICRA) (publié)
doi.org
arxiv.org
Encoder-Decoder Neural Architecture Optimization for Keyword Spotting
Tong Mo
Bang Liu
2021-06-03
ArXiv (prépublication)
arxiv.org
arxiv.org