186 citations · 500 across the 28 of their papers we have counts for
28 papers
REBAR: Retrieval-Based Reconstruction for Time-series Contrastive Learning
Maxwell A. Xu, Alexander Moreno, Hui Wei +2
The success of self-supervised contrastive learning hinges on identifying positive data pairs, such that when they are pushed together in embedding space, the space encodes useful…
Assessing the Impact of Context Inference Error and Partial Observability on RL Methods for Just-In-Time Adaptive Interventions
Karine Karine, Predrag Klasnja, Susan A. Murphy +1
Just-in-Time Adaptive Interventions (JITAIs) are a class of personalized health interventions developed within the behavioral science community. JITAIs aim to provide the right typ…
BayesLDM: A Domain-Specific Language for Probabilistic Modeling of Longitudinal Data
Karine Tung, Steven De La Torre, Mohamed El Mistiri +7
In this paper we present BayesLDM, a system for Bayesian longitudinal data modeling consisting of a high-level modeling language with specific features for modeling complex multiva…
Impact of Parameter Sparsity on Stochastic Gradient MCMC Methods for Bayesian Deep Learning
Meet P. Vadera, Adam D. Cobb, Brian Jalaian +1
Bayesian methods hold significant promise for improving the uncertainty quantification ability and robustness of deep neural network models. Recent research has seen the investigat…
Challenges and Opportunities in Approximate Bayesian Deep Learning for Intelligent IoT Systems
Meet P. Vadera, Benjamin M. Marlin
Approximate Bayesian deep learning methods hold significant promise for addressing several issues that occur when deploying deep learning components in intelligent systems, includi…
Heteroscedastic Temporal Variational Autoencoder For Irregular Time Series
Satya Narayan Shukla, Benjamin M. Marlin
Irregularly sampled time series commonly occur in several domains where they present a significant challenge to standard deep learning models. In this paper, we propose a new deep…