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
Maximizing Energy Efficiency With Channel Uncertainty Under Mutual Interference
Oct 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
Trinity: Trust, Resilience and Interpretability of Machine Learning Models
Jan 1, 2021
On DoF Conservation in MIMO Interference Cancellation based on Signal Strength in the Eigenspace
Jan 1, 2021
Minimizing AoI in a 5G-Based IoT Network Under Varying Channel Conditions
Jan 1, 2021
Maximize Spectrum Efficiency in Underlay Coexistence With Channel Uncertainty
Jan 1, 2021