3 citations · 6 across the 6 of their papers we have counts for
6 papers
Report for NSF Workshop on Algorithm-Hardware Co-design for Medical Applications
Peipei Zhou, Zheng Dong, Insup Lee +8
This report summarizes the discussions and recommendations from the NSF Workshop on Algorithm-Hardware Co-design for Medical Applications, held on September 26-27, 2024, in Pittsbu…
Memory-Consistent Neural Networks for Imitation Learning
Kaustubh Sridhar, Souradeep Dutta, Dinesh Jayaraman +2
Imitation learning considerably simplifies policy synthesis compared to alternative approaches by exploiting access to expert demonstrations. For such imitation policies, errors aw…
Curating Naturally Adversarial Datasets for Learning-Enabled Medical Cyber-Physical Systems
Sydney Pugh, Ivan Ruchkin, Insup Lee +1
Deep learning models have shown promising predictive accuracy for time-series healthcare applications. However, ensuring the robustness of these models is vital for building trustw…
A Novel Bayes' Theorem for Upper Probabilities
Michele Caprio, Yusuf Sale, Eyke Hüllermeier +1
In their seminal 1990 paper, Wasserman and Kadane establish an upper bound for the Bayes' posterior probability of a measurable set , when the prior lies in a class of probabili…
TRAQ: Trustworthy Retrieval Augmented Question Answering via Conformal Prediction
Shuo Li, Sangdon Park, Insup Lee +1
When applied to open-domain question answering, large language models (LLMs) frequently generate incorrect responses based on made-up facts, which are called $\textit{hallucination…
Detection of Adversarial Physical Attacks in Time-Series Image Data
Ramneet Kaur, Yiannis Kantaros, Wenwen Si +2
Deep neural networks (DNN) have become a common sensing modality in autonomous systems as they allow for semantically perceiving the ambient environment given input images. Neverth…