Uncertainty-Quantified Neurosymbolic AI for Open Set Recognition in Network Intrusion Detection
Oct 28, 2024
Decentralized Bayesian learning with Metropolis-adjusted Hamiltonian Monte Carlo
Aug 1, 2023
Reducing classifier overconfidence against adversaries through graph algorithms
Jul 1, 2023
Enhancing Resilience in Mobile Edge Computing Under Processing Uncertainty
Mar 1, 2023
The Methodological Pitfall of Dataset-Driven Research on Deep Learning: An IoT Example
Nov 1, 2022
URSABench: A System for Comprehensive Benchmarking of Bayesian Deep Neural Network Models and Inference Methods
URSABench is an open-source benchmarking suite for assessing Bayesian deep learning models and inference methods, focusing on uncertainty quantification, robustness, scalability, and accuracy in classification tasks for both server and edge GPUs.
Apr 1, 2022
Impact of Parameter Sparsity on Stochastic Gradient MCMC Methods for Bayesian Deep Learning
Feb 1, 2022
Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting
We introduce a new symmetric integration scheme for split Hamiltonian Monte Carlo (HMC) that enables efficient inference for Bayesian neural networks on large datasets. Our method outperforms stochastic gradient MCMC in accuracy and uncertainty quantification, demonstrating HMC as a viable option for large-scale machine learning problems.
Dec 1, 2021
Robust Decision-Making in the Internet of Battlefield Things Using Bayesian Neural Networks
Dec 1, 2021
Improving Differential Evolution through Bayesian Hyperparameter Optimization
Jun 1, 2021