43 citations · 169 across the 21 of their papers we have counts for
12 papers · 1 filter
A Simple and Effective Reinforcement Learning Method for Text-to-Image Diffusion Fine-tuning
Shashank Gupta, Chaitanya Ahuja, Tsung-Yu Lin +4
Reinforcement learning (RL)-based fine-tuning has emerged as a powerful approach for aligning diffusion models with black-box objectives. Proximal policy optimization (PPO) is a po…
BRIDLE: Generalized Self-supervised Learning with Quantization
Hoang M. Nguyen, Satya N. Shukla, Qiang Zhang +6
Self-supervised learning has been a powerful approach for learning meaningful representations from unlabeled data across various domains, reducing the reliance on large labeled dat…
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…
A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series
Satya Narayan Shukla, Benjamin M. Marlin
Irregularly sampled time series data arise naturally in many application domains including biology, ecology, climate science, astronomy, and health. Such data represent fundamental…
Multi-Time Attention Networks for Irregularly Sampled Time Series
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. This work is motivated by the analys…
Gaussian MRF Covariance Modeling for Efficient Black-Box Adversarial Attacks
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. Alt…
Simple and Efficient Hard Label Black-box Adversarial Attacks in Low Query Budget Regimes
Satya Narayan Shukla, Anit Kumar Sahu, Devin Willmott +1
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 l…
Integrating Physiological Time Series and Clinical Notes with Deep Learning for Improved ICU Mortality Prediction
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, la…
Assessing the Adversarial Robustness of Monte Carlo and Distillation Methods for Deep Bayesian Neural Network Classification
Meet P. Vadera, Satya Narayan Shukla, Brian Jalaian +1
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…
Black-box Adversarial Attacks with Bayesian Optimization
Satya Narayan Shukla, Anit Kumar Sahu, Devin Willmott +1
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 p…
Interpolation-Prediction Networks for Irregularly Sampled Time Series
Satya Narayan Shukla, Benjamin M. Marlin
In this paper, we present a new deep learning architecture for addressing the problem of supervised learning with sparse and irregularly sampled multivariate time series. The archi…
Modeling Irregularly Sampled Clinical Time Series
Satya Narayan Shukla, Benjamin M. Marlin
While the volume of electronic health records (EHR) data continues to grow, it remains rare for hospital systems to capture dense physiological data streams, even in the data-rich…