32 citations · 41 across the 8 of their papers we have counts for
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stat.ML2021
Bayesian Learning via Neural Schrödinger-Föllmer Flows
Francisco Vargas, Andrius Ovsianas, David Fernandes +3
In this work we explore a new framework for approximate Bayesian inference in large datasets based on stochastic control (i.e. Schrödinger bridges). We advocate stochastic control…
stat.ML2021★ 32 cited
Solving Schrödinger Bridges via Maximum Likelihood
Francisco Vargas, Pierre Thodoroff, Neil D. Lawrence +1
The Schrödinger bridge problem (SBP) finds the most likely stochastic evolution between two probability distributions given a prior stochastic evolution. As well as applications in…