2 citations · 2 across the 6 of their papers we have counts for
6 papers
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…
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…
Fractional Diffusion Bridge Models
Gabriel Nobis, Maximilian Springenberg, Arina Belova +5
We present Fractional Diffusion Bridge Models (FDBM), a novel generative diffusion bridge framework driven by an approximation of the rich and non-Markovian fractional Brownian mot…
Multimodal Generative Flows for LHC Jets
Darius A. Faroughy, Manfred Opper, Cesar Ojeda
Generative modeling of high-energy collisions at the Large Hadron Collider (LHC) offers a data-driven route to simulations, anomaly detection, among other applications. A central c…
Efficient Training of Neural SDEs Using Stochastic Optimal Control
Rembert Daems, Manfred Opper, Guillaume Crevecoeur +1
We present a hierarchical, control theory inspired method for variational inference (VI) for neural stochastic differential equations (SDEs). While VI for neural SDEs is a promisin…
Variational Inference for SDEs Driven by Fractional Noise
Rembert Daems, Manfred Opper, Guillaume Crevecoeur +1
We present a novel variational framework for performing inference in (neural) stochastic differential equations (SDEs) driven by Markov-approximate fractional Brownian motion (fBM)…