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
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Task Offloading with Uncertain Processing Cycles
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Improving Differential Evolution through Bayesian Hyperparameter Optimization
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Machine learning raw network traffic detection
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Fountain Coding for Information Protection in Tactical Networks
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AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filter
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On uncertainty and robustness in large-scale intelligent data fusion systems
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Generalized bayesian posterior expectation distillation for deep neural networks
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A Real-Time Solution for Underlay Coexistence with Channel Uncertainty
Dec 1, 2019