5 citations · 6 across the 3 of their papers we have counts for
3 papers
A Framework for Variational Inference of Lightweight Bayesian Neural Networks with Heteroscedastic Uncertainties
David J. Schodt, Ryan Brown, Michael Merritt +3
Obtaining heteroscedastic predictive uncertainties from a Bayesian Neural Network (BNN) is vital to many applications. Often, heteroscedastic aleatoric uncertainties are learned as…
Input Novelty as a Control Metric for Time Varying Linear Systems
Gautam Kumar, Delsin Menolascino, ShiNung Ching
This paper introduces a framework for quantitative characterization of the controllability of time-varying linear systems (or networks) in terms of input novelty. The motivation fo…
Controlling Linear Networks with Minimally Novel Inputs
Gautam Kumar, Delsin Menolascino, MohammadMehdi Kafashan +1
In this paper, we propose a novelty-based metric for quantitative characterization of the controllability of complex networks. This inherently bounded metric describes the average…