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Generative Flow Networks (or GFlowNets for short) are a family of probabilistic agents that learn to sample complex combinatorial structures through the lens of "inference as control".
Equation of state calculations by fast computing machines
Nicholas Metropolis, Arianna W Rosenbluth, Marshall N Rosenbluth, Augusta H Teller, and Edward Teller · 1953
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Monte carlo sampling methods using markov chains and their applications
W Keith Hastings · 1970
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An introduction to mcmc for machine learning
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Diederik P Kingma and Max Welling · 2013
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Generative adversarial nets
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Learning continuous control policies by stochastic value gradients
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Adam: A method for stochastic optimization
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Human-level control through deep reinforcement learning
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Reinforcement learning with deep energy-based policies
Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, and Sergey Levine · 2017
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Diversity in recommender systems–a survey
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Attention is all you need
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Addressing function approximation error in actor-critic methods
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
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Revisiting the arcade learning environment: Evaluation protocols and open problems for general agents
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Large-scale de novo oligonucleotide synthesis for whole-genome synthesis and data storage: Challenges and opportunities
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Yutong Xie, Chence Shi, Hao Zhou, Yuwei Yang, Weinan Zhang, Yong Yu, and Lei Li · 2021
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Bayesian structure learning with generative flow networks
Tristan Deleu, António Góis, Chris Emezue, Mansi Rankawat, Simon Lacoste-Julien, Stefan Bauer, and Yoshua Bengio · 2022
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Gflowout: Dropout with generative flow networks
Dianbo Liu, Moksh Jain, Bonaventure F. P. Dossou, Qianli Shen, Salem Lahlou, Anirudh Goyal, Nikolay Malkin, Chris C. Emezue, Dinghuai Zhang, Nadhir Hassen, Xu Ji, Kenji Kawaguchi, and Yoshua Bengio · 2022
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Learning GFlowNets from partial episodes for improved convergence and stability
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Reinforcement learning: An introduction
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Denoising diffusion probabilistic models
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Planning in stochastic environments with a learned model
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Bayesian learning of causal structure and mechanisms with GFlowNets and variational bayes
Mizu Nishikawa-Toomey, Tristan Deleu, Jithendaraa Subramanian, Yoshua Bengio, and Laurent Charlin · 2022
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Generative augmented flow networks
Ling Pan, Dinghuai Zhang, Aaron Courville, Longbo Huang, and Yoshua Bengio · 2022
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You can’t count on luck: Why decision transformers fail in stochastic environments
Keiran Paster, Sheila McIlraith, and Jimmy Ba · 2022
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Design-bench: Benchmarks for data-driven offline model-based optimization
Brandon Trabucco, Xinyang Geng, Aviral Kumar, and Sergey Levine · 2022
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Dichotomy of control: Separating what you can control from what you cannot
Mengjiao Yang, Dale Schuurmans, Pieter Abbeel, and Ofir Nachum · 2022
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Better training of gflownets with local credit and incomplete trajectories
Ling Pan, Nikolay Malkin, Dinghuai Zhang, and Yoshua Bengio · 2023
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