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Generative Flow Networks (GFlowNets; GFNs) are a family of energy-based generative methods for combinatorial objects, capable of generating diverse and high-utility samples.
Learning to predict by the methods of temporal differences
Richard S Sutton · 1988
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Q-learning
Christopher JCH Watkins and Peter Dayan · 1992
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Molecular similarity: a key technique in molecular informatics
Andreas Bender and Robert C Glen · 2004
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Viennarna package 2.0
Ronny Lorenz, Stephan H Bernhart, Christian Höner zu Siederdissen, Hakim Tafer, Christoph Flamm, Peter F Stadler, and Ivo L Hofacker · 2011
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Rdkit documentation
Greg Landrum · 2013
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld · 2014
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
Cited alongside, same era.
Rainbow: Combining improvements in deep reinforcement learning
Matteo Hessel, Joseph Modayil, Hado Van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, and David Silver · 2018
Cited alongside, same era.
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
Cited alongside, same era.
Adalead: A simple and robust adaptive greedy search algorithm for sequence design
Sam Sinai, Richard Wang, Alexander Whatley, Stewart Slocum, Elina Locane, and Eric D Kelsic · 2020
Cited alongside, same era.
Molecular mechanics-driven graph neural network with multiplex graph for molecular structures
Shuo Zhang, Yang Liu, and Lei Xie · 2020
Cited alongside, same era.
Dgfn: Double generative flow networks
Elaine Lau, Nikhil Vemgal, Doina Precup, and Emmanuel Bengio · 2023
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Learning gflownets from partial episodes for improved convergence and stability
Kanika Madan, Jarrid Rector-Brooks, Maksym Korablyov, Emmanuel Bengio, Moksh Jain, Andrei Cristian Nica, Tom Bosc, Yoshua Bengio, and Nikolay Malkin · 2023
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Thompson sampling for improved exploration in gflownets
Jarrid Rector-Brooks, Kanika Madan, Moksh Jain, Maksym Korablyov, Cheng-Hao Liu, Sarath Chandar, Nikolay Malkin, and Yoshua Bengio · 2023
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Towards understanding and improving gflownet training, 2023
Max W. Shen, Emmanuel Bengio, Ehsan Hajiramezanali, Andreas Loukas, Kyunghyun Cho, and Tommaso Biancalani · 2023
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Biological sequence design with gflownets
Moksh Jain, Emmanuel Bengio, Alex Hernandez-Garcia, Jarrid Rector-Brooks, Bonaventure FP Dossou, Chanakya Ajit Ekbote, Jie Fu, Tianyu Zhang, Michael Kilgour, Dinghuai Zhang, et al · 2022
Cited alongside, same era.
Unifying generative models with gflownets
Dinghuai Zhang, Ricky TQ Chen, Nikolay Malkin, and Yoshua Bengio · 2022
Cited alongside, same era.
Moksh Jain, Sharath Chandra Raparthy, Alex Hernández-Garcia, Jarrid Rector-Brooks, Yoshua Bengio, Santiago Miret, and Emmanuel Bengio · 2023
Cited alongside, same era.
A theory of continuous generative flow networks
Salem Lahlou, Tristan Deleu, Pablo Lemos, Dinghuai Zhang, Alexandra Volokhova, Alex Hernández-Garcıa, Léna Néhale Ezzine, Yoshua Bengio, and Nikolay Malkin · 2023
Cited alongside, same era.
Flow network based generative models for non-iterative diverse candidate generation
Emmanuel Bengio, Moksh Jain, Maksym Korablyov, Doina Precup, and Yoshua Bengio
Cited in the paper.
Yoshua Bengio, Tristan Deleu, Edward J Hu, Salem Lahlou, Mo Tiwari, and Emmanuel Bengio
Cited in the paper.
Learning to scale logits for temperature-conditional gflownets
Minsu Kim, Joohwan Ko, Dinghuai Zhang, Ling Pan, Taeyoung Yun, Woochang Kim, Jinkyoo Park, and Yoshua Bengio
Cited in the paper.
Daniil Tiapkin, Nikita Morozov, Alexey Naumov, and Dmitry Vetrov · 2023
Later among the works it cites.
An empirical study of the effectiveness of using a replay buffer on mode discovery in gflownets
Nikhil Vemgal, Elaine Lau, and Doina Precup · 2023
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Let the flows tell: Solving graph combinatorial problems with gflownets
Dinghuai Zhang, Hanjun Dai, Nikolay Malkin, Aaron C Courville, Yoshua Bengio, and Ling Pan · 2023
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Discrete probabilistic inference as control in multi-path environments
Tristan Deleu, Padideh Nouri, Nikolay Malkin, Doina Precup, and Yoshua Bengio · 2024
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Maximum entropy gflownets with soft q-learning
Sobhan Mohammadpour, Emmanuel Bengio, Emma Frejinger, and Pierre-Luc Bacon · 2024
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