Hydra: An Agentic Reasoning Approach for Enhancing Adversarial Robustness and Mitigating Hallucinations in Vision-Language Models
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Jul 1, 2025
Towards Interpretable Adversarial Examples via Sparse Adversarial Attack
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Jan 1, 2025
Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning
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Jan 1, 2025
Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression
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Jan 1, 2025
Decentralized Bayesian learning with Metropolis-adjusted Hamiltonian Monte Carlo
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Reducing classifier overconfidence against adversaries through graph algorithms
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Enhancing Resilience in Mobile Edge Computing Under Processing Uncertainty
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Maximizing Energy Efficiency With Channel Uncertainty Under Mutual Interference
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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