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cs.LG2026
Introduction to Stochastic Differential Equations for Generative Machine Learning: A Variational Perspective
Ole Winther, Paul Jeha, Sander Dieleman +3
The use of ordinary and stochastic differential equations has led to substantial progress in generative machine learning with applications to, for example, image, video and biomole…
cs.LG2026
Variational Inference for Lévy Process-Driven SDEs via Neural Tilting
Yaman Kindap, Manfred Opper, Benjamin Dupuis +2
Modelling extreme events and heavy-tailed phenomena is central to building reliable predictive systems in domains such as finance, climate science, and safety-critical AI. While Lé…