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Generative flow networks (GFlowNets) are amortized variational inference algorithms that are trained to sample from unnormalized target distributions over compositional objects.
An Introduction to Sequential Monte Carlo Methods , pp. 3–14
Doucet, A., Freitas, N., and Gordon, N · 2001
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Annealed importance sampling
Neal, R. M · 2001
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The Langevin Equation: With Applications to Stochastic Problems in Physics, Chemistry and Electrical Engineering
Coffey, W., Kalmykov, Y., and Waldron, J · 2004
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General irreducible Markov chains and non-negative operators
Nummelin, E · 2004
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Inference in hidden markov models
Cappé, O., Moulines, E., and Rydén, T · 2009
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The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo
Hoffman, M. D. and Gelman, A · 2011
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MCMC using Hamiltonian dynamics
Neal, R. M · 2012
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Molview, 2014
Bergwerf, H · 2014
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Markov chains on measurable spaces
Petritis, D · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J. N., Weiss, E. A., Maheswaranathan, N., and Ganguli, S · 2015
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A systematic study of minima in alanine dipeptide
Mironov, V., Alexeev, Y., Mulligan, V. K., and Fedorov, D. G · 2018
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Stochastic normalizing flows
Wu, H., Köhler, J., and Noé, F · 2020
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BCD Nets: Scalable variational approaches for Bayesian causal discovery
Cundy, C., Grover, A., and Ermon, S · 2021
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DiBS: Differentiable Bayesian structure learning
Lorch, L., Rothfuss, J., Schölkopf, B., and Krause, A · 2021
Cited alongside, same era.
Bayesian structure learning with generative flow networks
Deleu, T., Góis, A., Emezue, C., Rankawat, M., Lacoste-Julien, S., Bauer, S., and Bengio, Y · 2022
Cited alongside, same era.
Learning GFlowNets from partial episodes for improved convergence and stability
Madan, K., Rector-Brooks, J., Korablyov, M., Bengio, E., Jain, M., Nica, A., Bosc, T., Bengio, Y., and Malkin, N · 2022
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Trajectory balance: Improved credit assignment in GFlowNets
Malkin, N., Jain, M., Bengio, E., Sun, C., and Bengio, Y · 2022
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Bayesian learning of causal structure and mechanisms with GFlowNets and variational bayes
Nishikawa-Toomey, M., Deleu, T., Subramanian, J., Bengio, Y., and Charlin, L · 2022
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Generative augmented flow networks
Pan, L., Zhang, D., Courville, A., Huang, L., and Bengio, Y · 2022
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Conformer search using SE3-transformers and imitation learning
Thiede, L., Miret, S., Sadowski, K., Xu, H., Phielipp, M., and Aspuru-Guzik, A · 2022
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Generative flow networks for discrete probabilistic modeling
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Score-based generative modeling with critically-damped langevin diffusion
Dockhorn, T., Vahdat, A., and Kreis, K · 2022
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Stochastic normalizing flows for inverse problems: A Markov chains viewpoint
Hagemann, P., Hertrich, J., and Steidl, G · 2022
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Biological sequence design with GFlowNets
Jain, M., Bengio, E., Hernandez-Garcia, A., Rector-Brooks, J., Dossou, B. F., Ekbote, C., Fu, J., Zhang, T., Kilgour, M., Zhang, D., Simine, L., Das, P., and Bengio, Y · 2022
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Torsional diffusion for molecular conformer generation
Jing, B., Corso, G., Jeffrey Chang, R. B., and Jaakkola, T · 2022
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Flow matching for generative modeling
Lipman, Y., Chen, R. T. Q., Ben-Hamu, H., Nickel, M., and Le, M · 2022
Cited alongside, same era.
Flow network based generative models for non-iterative diverse candidate generation
Bengio, E., Jain, M., Korablyov, M., Precup, D., and Bengio, Y
Cited in the paper.
Zhang, D., Malkin, N., Liu, Z., Volokhova, A., Courville, A., and Bengio, Y · 2022
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Path integral sampler: a stochastic control approach for sampling
Zhang, Q. and Chen, Y · 2022
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A variational perspective on generative flow networks
Zimmermann, H., Lindsten, F., van de Meent, J.-W., and Naesseth, C. A · 2022
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CFlowNets: Continuous control with generative flow networks
Li, Y., Luo, S., Wang, H., and Hao, J · 2023
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GFlowNets and variational inference
Malkin, N., Lahlou, S., Deleu, T., Ji, X., Hu, E. J., Everett, K. E., Zhang, D., and Bengio, Y · 2023
Closest in time.