The use of learning based components in cyber-physical systems (CPS) has created a
gamut of possible avenues to use high dimensional real world signals generated from …

Uncertainties in machine learning are a significant roadblock for its application in safety-
critical cyber-physical systems (CPS). One source of uncertainty arises from distribution …

Incorporating learning based components in the current state-of-the-art cyber-physical
systems (CPS) has been a challenge due to the brittleness of the underlying deep neural …

We focus on the challenge of out-of-distribution (OOD) detection in deep learning models, a
crucial aspect in ensuring reliability. Despite considerable effort, the problem remains …

Abstract Cyber-Physical Systems (CPSs) are ubiquitous through our interactions with
applications such as smart homes, medical devices, avionics, and automobiles. However …

Deep generative models (DGM) combine the deep neural networks with generative models
to learn the underlying generation mechanism of the data of interest. This has emerged as a …

Prediction of multi-dimensional labels plays an important role in machine learning problems.
We found that the classical binary labels could not reflect the contents and their relationships …