A theory of continuous generative flow networks

S Lahlou, T Deleu, P Lemos, D Zhang… - International …, 2023 - proceedings.mlr.press
Generative flow networks (GFlowNets) are amortized variational inference algorithms that
are trained to sample from unnormalized target distributions over compositional objects. A …
被引用次数：55 相关文章
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[PDF][PDF] A Theory of Continuous Generative Flow Networks

S Lahlou, T Deleu, P Lemos, D Zhang, A Volokhova… - proceedings.mlr.press
Generative flow networks (GFlowNets) are amortized variational inference algorithms that
are trained to sample from unnormalized target distributions over compositional objects. A …

A theory of continuous generative flow networks

S Lahlou, T Deleu, P Lemos, D Zhang… - arXiv preprint arXiv …, 2023 - arxiv.org
Generative flow networks (GFlowNets) are amortized variational inference algorithms that
are trained to sample from unnormalized target distributions over compositional objects. A …

A theory of continuous generative flow networks

S Lahlou, T Deleu, P Lemos, D Zhang… - arXiv e …, 2023 - ui.adsabs.harvard.edu
Generative flow networks (GFlowNets) are amortized variational inference algorithms that
are trained to sample from unnormalized target distributions over compositional objects. A …

A theory of continuous generative flow networks

S Lahlou, T Deleu, P Lemos, D Zhang, A Volokhova… - openreview.net
Generative flow networks (GFlowNets) are amortized variational inference algorithms that
are trained to sample from unnormalized target distributions over compositional objects. A …

A theory of continuous generative flow networks

S Lahlou, T Deleu, P Lemos, D Zhang… - Proceedings of the 40th …, 2023 - dl.acm.org
Generative flow networks (GFlowNets) are amortized variational inference algorithms that
are trained to sample from unnormalized target distributions over compositional objects. A …