Action Matching: A Variational Method for Learning Stochastic Dynamics from Samples

By Kirill Neklyudov, Daniel de Souza Severo, Alireza Makhzani. 2022

31 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

Citations in the corpus

• Flow Matching for Generative Modeling (2022, neutral): “Lastly, concurrently to our work (Liu et al., 2022; Albergo & Vanden-Eijnden, 2022) arrived at similar conditional objectives for simulation-free training of CNFs, while Neklyudov et al. (2023) derived an implicit objective when u t is assumed to be a gradient field.”
• Improving and generalizing flow-based generative models with minibatch optimal transport (2023, unknown): “…simulation-free training of CNFs considers algorithms that are equivalent to CFM with Gaussian source distribution (Rozen et al., 2021; Ben-Hamu et al., 2022; Lipman et al., 2023) or independent samples from q 0 , q 1 (Albergo & Vanden-Eijnden, 2023; Albergo et al., 2023; Neklyudov et al., 2022).”
• Building Normalizing Flows with Stochastic Interpolants (2022, unknown): “This objective is, up to a sign and a factor 2, the KILBO objective introduced in Neklyudov et al. (2022).”