Large-Scale Intrinsic Functional Brain Organization Emerges from Three Canonical Spatiotemporal Patterns
Taylor Bolt
Jason S. Nomi
Catie Chang
B.T. Yeo
Lucina Q. Uddin
Shella Keilholz
A parsimonious description of global functional brain organization in three spatiotemporal patterns
Taylor Bolt
Jason S. Nomi
Jorge A. Salas
Catie Chang
B. T. Thomas Yeo
Lucina Q. Uddin
S. Keilholz
A Data Mining Analysis of Cross-Regional Study of Apparel Consumption
Osmud Rahman
Smart about medications (SAM): a digital solution to enhance medication management following hospital discharge
Santiago Márquez Fosser
Nadar Mahmoud
Bettina Habib
Daniala L Weir
Fiona Chan
Rola El Halabieh
Jeanne Vachon
Manish Thakur
Thai Tran
Melissa Bustillo
Caroline Beauchamp
André Bonnici
Robyn Tamblyn
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 C Bernhardt
SPeCiaL: Self-Supervised Pretraining for Continual Learning
Lucas Caccia
Improving Continuous Normalizing Flows using a Multi-Resolution Framework
Vikram Voleti
Chris Finlay
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.
Randomized Exploration for Reinforcement Learning with General Value Function Approximation
Haque Ishfaq
Qiwen Cui
Viet Huy Nguyen
Alex Ayoub
Zhuoran Yang
Zhaoran Wang
Lin F. Yang
We propose a model-free reinforcement learning algorithm inspired by the popular randomized least squares value iteration (RLSVI) algorithm … (voir plus)as well as the optimism principle. Unlike existing upper-confidence-bound (UCB) based approaches, which are often computationally intractable, our algorithm drives exploration by simply perturbing the training data with judiciously chosen i.i.d. scalar noises. To attain optimistic value function estimation without resorting to a UCB-style bonus, we introduce an optimistic reward sampling procedure. When the value functions can be represented by a function class
Variational Causal Networks: Approximate Bayesian Inference over Causal Structures
Yashas Annadani
Jonas Rothfuss
Alexandre Lacoste
Nino Scherrer
Anirudh Goyal
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.
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
Iulian V. Serban
Ekaterina Kochmar
Incorporating dynamic flight network in SEIR to model mobility between populations
Xiaoye Ding
Shenyang Huang
Abby Leung
RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting
Soumyasundar Pal
Liheng Ma
Yingxue Zhang
M. Coates
Spatio-temporal forecasting has numerous applications in analyzing wireless, traffic, and financial networks. Many classical statistical mod… (voir plus)els often fall short in handling the complexity and high non-linearity present in time-series data. Recent advances in deep learning allow for better modelling of spatial and temporal dependencies. While most of these models focus on obtaining accurate point forecasts, they do not characterize the prediction uncertainty. In this work, we consider the time-series data as a random realization from a nonlinear state-space model and target Bayesian inference of the hidden states for probabilistic forecasting. We use particle flow as the tool for approximating the posterior distribution of the states, as it is shown to be highly effective in complex, high-dimensional settings. Thorough experimentation on several real world time-series datasets demonstrates that our approach provides better characterization of uncertainty while maintaining comparable accuracy to the state-of-the art point forecasting methods.