A Synergistic Approach In Network Intrusion Detection By Neurosymbolic AI
Jun 1, 2024
Decentralized Bayesian learning with Metropolis-adjusted Hamiltonian Monte Carlo
Aug 1, 2023
Reducing classifier overconfidence against adversaries through graph algorithms
Jul 1, 2023
Shedding Light on Darkness: Enhancing Object Detection Robustness with Synthetic Perturbations for Real-world Challenges
Jun 1, 2023
Improving Object Detection Robustness against Natural Perturbations through Synthetic Data Augmentation
Jun 1, 2023
Enhancing object detection robustness: A synthetic and natural perturbation approach
Apr 1, 2023
Enhancing Resilience in Mobile Edge Computing Under Processing Uncertainty
Mar 1, 2023
Adversarial Machine Learning: A New Threat Paradigm for Next-generation Wireless Communications
Jan 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