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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...
11/30/2020 ∙ by Satya Narayan Shukla, et al. ∙
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Gaussian MRF Covariance Modeling for Efficient Black-Box Adversarial Attacks
We study the problem of generating adversarial examples in a black-box s...
10/08/2020 ∙ by Anit Kumar Sahu, et al. ∙
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Hard Label Black-box Adversarial Attacks in Low Query Budget Regimes
We focus on the problem of black-box adversarial attacks, where the aim ...
07/13/2020 ∙ by Satya Narayan Shukla, et al. ∙
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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...
03/24/2020 ∙ by Satya Narayan Shukla, et al. ∙
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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...
02/07/2020 ∙ by Meet P. Vadera, et al. ∙
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Black-box Adversarial Attacks with Bayesian Optimization
We focus on the problem of black-box adversarial attacks, where the aim ...
09/30/2019 ∙ by Satya Narayan Shukla, et al. ∙
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Interpolation-Prediction Networks for Irregularly Sampled Time Series
In this paper, we present a new deep learning architecture for addressin...
09/13/2019 ∙ by Satya Narayan Shukla, et al. ∙
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Modeling Irregularly Sampled Clinical Time Series
While the volume of electronic health records (EHR) data continues to gr...
12/03/2018 ∙ by Satya Narayan Shukla, et al. ∙
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