Neurosymbolic AI in Cybersecurity: Bridging Pattern Recognition and Symbolic Reasoning
Oct 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
The Methodological Pitfall of Dataset-Driven Research on Deep Learning: An IoT Example
Nov 1, 2022
EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks
Apr 1, 2022
Runtime Monitoring of Deep Neural Networks Using Top-Down Context Models Inspired by Predictive Processing and Dual Process Theory
Jan 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
Task Offloading with Uncertain Processing Cycles
Jul 1, 2021
Improving Differential Evolution through Bayesian Hyperparameter Optimization
Jun 1, 2021