AI RESEARCH
Improving Infinitely Deep Bayesian Neural Networks with Nesterov's Accelerated Gradient Method
arXiv CS.LG
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ArXi:2603.25024v1 Announce Type: cross As a representative continuous-depth neural network approach, stochastic differential equation (SDE)-based Bayesian neural networks (BNNs) have attracted considerable attention due to their solid theoretical foundations and strong potential for real-world applications. However, their reliance on numerical SDE solvers inevitably incurs a large number of function evaluations (NFEs), resulting in high computational cost and occasional convergence instability.