Search Results for author: Satya Narayan Shukla

Found 10 papers, 7 papers with code

Heteroscedastic Temporal Variational Autoencoder For Irregularly Sampled Time Series

1 code implementation23 Jul 2021 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.

Time Series

Multi-Time Attention Networks for Irregularly Sampled Time Series

1 code implementation25 Jan 2021 Satya Narayan Shukla, Benjamin M. Marlin

Irregular sampling occurs in many time series modeling applications where it presents a significant challenge to standard deep learning models.

Time Series

A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series

no code implementations30 Nov 2020 Satya Narayan Shukla, Benjamin M. Marlin

Such data represent fundamental challenges to many classical models from machine learning and statistics due to the presence of non-uniform intervals between observations.

Time Series

Gaussian MRF Covariance Modeling for Efficient Black-Box Adversarial Attacks

1 code implementation8 Oct 2020 Anit Kumar Sahu, Satya Narayan Shukla, J. Zico Kolter

We study the problem of generating adversarial examples in a black-box setting, where we only have access to a zeroth order oracle, providing us with loss function evaluations.

Simple and Efficient Hard Label Black-box Adversarial Attacks in Low Query Budget Regimes

1 code implementation13 Jul 2020 Satya Narayan Shukla, Anit Kumar Sahu, Devin Willmott, J. Zico Kolter

We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples for deep learning models solely based on information limited to output label~(hard label) to a queried data input.

Integrating Physiological Time Series and Clinical Notes with Deep Learning for Improved ICU Mortality Prediction

no code implementations24 Mar 2020 Satya Narayan Shukla, Benjamin M. Marlin

Intensive Care Unit Electronic Health Records (ICU EHRs) store multimodal data about patients including clinical notes, sparse and irregularly sampled physiological time series, lab results, and more.

ICU Mortality Time Series

Assessing the Adversarial Robustness of Monte Carlo and Distillation Methods for Deep Bayesian Neural Network Classification

no code implementations7 Feb 2020 Meet P. Vadera, Satya Narayan Shukla, Brian Jalaian, Benjamin M. Marlin

In this paper, we consider the problem of assessing the adversarial robustness of deep neural network models under both Markov chain Monte Carlo (MCMC) and Bayesian Dark Knowledge (BDK) inference approximations.

Adversarial Robustness General Classification

Black-box Adversarial Attacks with Bayesian Optimization

1 code implementation30 Sep 2019 Satya Narayan Shukla, Anit Kumar Sahu, Devin Willmott, J. Zico Kolter

We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples using information limited to loss function evaluations of input-output pairs.

Interpolation-Prediction Networks for Irregularly Sampled Time Series

1 code implementation ICLR 2019 Satya Narayan Shukla, Benjamin M. Marlin

The interpolation network allows for information to be shared across multiple dimensions of a multivariate time series during the interpolation stage, while any standard deep learning model can be used for the prediction network.

Length-of-Stay prediction Mortality Prediction +2

Modeling Irregularly Sampled Clinical Time Series

1 code implementation3 Dec 2018 Satya Narayan Shukla, Benjamin M. Marlin

In this paper, we present a new deep learning architecture for addressing this problem based on the use of a semi-parametric interpolation network followed by the application of a prediction network.

Length-of-Stay prediction Time Series

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