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Generative Flow Networks (GFlowNets) are amortized sampling methods that learn a distribution over discrete objects proportional to their rewards.
Monte carlo sampling methods using markov chains and their applications
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Denoising diffusion probabilistic models
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Flow network based generative models for non-iterative diverse candidate generation
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Bayesian structure learning with generative flow networks
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Batch multi-fidelity active learning with budget constraints
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Dyngfn: Bayesian dynamic causal discovery using generative flow networks
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Gflownet foundations
Yoshua Bengio, Salem Lahlou, Tristan Deleu, Edward J. Hu, Mo Tiwari, and Emmanuel Bengio · 2023
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Joint bayesian inference of graphical structure and parameters with a single generative flow network
Tristan Deleu, Mizu Nishikawa-Toomey, Jithendaraa Subramanian, Nikolay Malkin, Laurent Charlin, and Yoshua 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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GFlowNets and variational inference
Nikolay Malkin, Salem Lahlou, Tristan Deleu, Xu Ji, Edward J. Hu, Katie Elizabeth Everett, Dinghuai Zhang, and Yoshua Bengio · 2023
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Trajectory balance: Improved credit assignment in gflownets
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Generative augmented flow networks
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Biological sequence design with gflownets
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Multi-objective gflownets
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Gflownets for ai-driven scientific discovery
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GFlowNets for AI-driven scientific discovery
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Thompson sampling for improved exploration in gflownets
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Towards understanding and improving GFlowNet training
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