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06/04/2023
Heteroskedastic Geospatial Tracking with Distributed Camera Networks
Visual object tracking has seen significant progress in recent years. Ho...
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05/17/2023
Assessing the Impact of Context Inference Error and Partial Observability on RL Methods for Just-In-Time Adaptive Interventions
Just-in-Time Adaptive Interventions (JITAIs) are a class of personalized...
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09/12/2022
BayesLDM: A Domain-Specific Language for Probabilistic Modeling of Longitudinal Data
In this paper we present BayesLDM, a system for Bayesian longitudinal da...
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02/08/2022
Impact of Parameter Sparsity on Stochastic Gradient MCMC Methods for Bayesian Deep Learning
Bayesian methods hold significant promise for improving the uncertainty ...
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12/03/2021
Challenges and Opportunities in Approximate Bayesian Deep Learning for Intelligent IoT Systems
Approximate Bayesian deep learning methods hold significant promise for ...
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07/23/2021
Heteroscedastic Temporal Variational Autoencoder For Irregularly Sampled Time Series
Irregularly sampled time series commonly occur in several domains where ...
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06/13/2021
Post-hoc loss-calibration for Bayesian neural networks
Bayesian decision theory provides an elegant framework for acting optima...
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01/25/2021
Multi-Time Attention Networks for Irregularly Sampled Time Series
Irregular sampling occurs in many time series modeling applications wher...
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11/30/2020
A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series: From Discretization to Attention and Invariance
Irregularly sampled time series data arise naturally in many application...
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08/17/2020
Learning from Irregularly-Sampled Time Series: A Missing Data Perspective
Irregularly-sampled time series occur in many domains including healthca...
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07/08/2020
URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks
While deep learning methods continue to improve in predictive accuracy o...
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05/16/2020
Generalized Bayesian Posterior Expectation Distillation for Deep Neural Networks
In this paper, we present a general framework for distilling expectation...
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03/24/2020
Integrating Physiological Time Series and Clinical Notes with Deep Learning for Improved ICU Mortality Prediction
Intensive Care Unit Electronic Health Records (ICU EHRs) store multimoda...
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02/07/2020
Assessing the Adversarial Robustness of Monte Carlo and Distillation Methods for Deep Bayesian Neural Network Classification
In this paper, we consider the problem of assessing the adversarial robu...
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09/13/2019
Interpolation-Prediction Networks for Irregularly Sampled Time Series
In this paper, we present a new deep learning architecture for addressin...
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06/04/2019
Assessing the Robustness of Bayesian Dark Knowledge to Posterior Uncertainty
Bayesian Dark Knowledge is a method for compressing the posterior predic...
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02/14/2019
Integrating Propositional and Relational Label Side Information for Hierarchical Zero-Shot Image Classification
Zero-shot learning (ZSL) is one of the most extreme forms of learning fr...
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12/03/2018
Modeling Irregularly Sampled Clinical Time Series
While the volume of electronic health records (EHR) data continues to gr...
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11/07/2016
Learning Time Series Detection Models from Temporally Imprecise Labels
In this paper, we consider a new low-quality label learning problem: lea...
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07/30/2016