Uncertainty-Quantified Neurosymbolic AI for Open Set Recognition in Network Intrusion Detection
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Mitigating Large Vision-Language Model Hallucination at Post-hoc via Multi-agent System
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A Neuro-Symbolic Artificial Intelligence Network Intrusion Detection System
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Neurosymbolic AI in Cybersecurity: Bridging Pattern Recognition and Symbolic Reasoning
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Shedding Light on Darkness: Enhancing Object Detection Robustness with Synthetic Perturbations for Real-world Challenges
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Improving Object Detection Robustness against Natural Perturbations through Synthetic Data Augmentation
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The Methodological Pitfall of Dataset-Driven Research on Deep Learning: An IoT Example
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EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks
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Runtime Monitoring of Deep Neural Networks Using Top-Down Context Models Inspired by Predictive Processing and Dual Process Theory
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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