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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 function or a reward.
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Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation
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Heiko Zimmermann, Hao Wu, Babak Esmaeili, and Jan-Willem van de Meent · 2021
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Bayesian Structure Learning with Generative Flow Networks
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Yoshua Bengio, Tristan Deleu, Edward J. Hu, Salem Lahlou, Mo Tiwari, and Emmanuel Bengio
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Trajectory Balance: Improved Credit Assignment in GFlowNets
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GFlowNets and variational inference, October 2022b
Nikolay Malkin, Salem Lahlou, Tristan Deleu, Xu Ji, Edward Hu, Katie Everett, Dinghuai Zhang, and Yoshua Bengio
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Tristan Deleu, António Góis, Chris Chinenye Emezue, Mansi Rankawat, Simon Lacoste-Julien, Stefan Bauer, and Yoshua Bengio · 2022
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Improving Generative Flow Networks with Path Regularization, September 2022
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Biological Sequence Design with GFlowNets, March 2022
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Learning GFlowNets from partial episodes for improved convergence and stability, September 2022
Kanika Madan, Jarrid Rector-Brooks, Maksym Korablyov, Emmanuel Bengio, Moksh Jain, Andrei Nica, Tom Bosc, Yoshua Bengio, and Nikolay Malkin · 2022
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