A new variational inference method uses neural networks to tilt Lévy measures, enabling scalable posterior inference for jump processes while preserving their discontinuous structure.
Exact gradients for stochastic spiking neural networks driven by rough signals
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Variational Inference for L\'evy Process-Driven SDEs via Neural Tilting
A new variational inference method uses neural networks to tilt Lévy measures, enabling scalable posterior inference for jump processes while preserving their discontinuous structure.