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We study the problem of training diffusion models to sample from a distribution with a given unnormalized density or energy function.
Neural stochastic differential equations: Deep latent Gaussian models in the diffusion limit
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Rezende, D. and Mohamed, S · 2015
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Hoffman, M., Sountsov, P., Dillon, J. V., Langmore, I., Tran, D., and Vasudevan, S · 2019
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Noé, F., Olsson, S., Köhler, J., and Wu, H · 2019
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Applied stochastic differential equations
Särkkä, S. and Solin, A · 2019
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Relaxing bijectivity constraints with continuously indexed normalising flows
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Gao, C., Isaacson, J., and Krause, C · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Stochastic optimal control for collective variable free sampling of molecular transition paths
Holdijk, L., Du, Y., Hooft, F., Jaini, P., Ensing, B., and Welling, M · 2023
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Learning to scale logits for temperature-conditional GFlowNets
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Amortized variational inference for simple hierarchical models
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Generative flow networks as entropy-regularized RL
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Denoising diffusion samplers
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A variational perspective on generative flow networks
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Discrete probabilistic inference as control in multi-path environments
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