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A Variational Perspective on Generative Flow Networks
Heiko Zimmermann, Fredrik Lindsten, Jan-Willem van de Meent +1
Generative flow networks (GFNs) are a class of models for sequential sampling of composite objects, which approximate a target distribution that is defined in terms of an energy fu…
Variational Combinatorial Sequential Monte Carlo Methods for Bayesian Phylogenetic Inference
Antonio Khalil Moretti, Liyi Zhang, Christian A. Naesseth +3
Bayesian phylogenetic inference is often conducted via local or sequential search over topologies and branch lengths using algorithms such as random-walk Markov chain Monte Carlo (…
Markovian Score Climbing: Variational Inference with KL(p||q)
Christian A. Naesseth, Fredrik Lindsten, David Blei
Modern variational inference (VI) uses stochastic gradients to avoid intractable expectations, enabling large-scale probabilistic inference in complex models. VI posits a family of…