13 citations · 24 across the 11 of their papers we have counts for
3 papers · 1 filter
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…
A Convergence Analysis of Approximate Message Passing with Non-Separable Functions and Applications to Multi-Class Classification
Burak Çakmak, Yue M. Lu, Manfred Opper
Motivated by the recent application of approximate message passing (AMP) to the analysis of convex optimizations in multi-class classifications [Loureiro, et. al., 2021], we presen…
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)…