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GFlowNets are probabilistic models that sequentially generate compositional structures through a stochastic policy.
Optimization by simulated annealing
Kirkpatrick, S., Gelatt Jr, C. D., and Vecchi, M. P · 1983
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Asynchronous methods for deep reinforcement learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., and Kavukcuoglu, K · 2016
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Deep exploration via bootstrapped dqn
Osband, I., Blundell, C., Pritzel, A., and Van Roy, B · 2016
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Reinforcement learning with deep energy-based policies
Haarnoja, T., Tang, H., Abbeel, P., and Levine, S · 2017
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Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
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Thermometer encoding: One hot way to resist adversarial examples
Buckman, J., Roy, A., Raffel, C., and Goodfellow, I · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Haarnoja, T., Zhou, A., Abbeel, P., and Levine, S · 2018
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Approximate inference in discrete distributions with monte carlo tree search and value functions
Buesing, L., Heess, N., and Weber, T · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Adalead: A simple and robust adaptive greedy search algorithm for sequence design
Sinai, S., Wang, R., Whatley, A., Slocum, S., Locane, E., and Kelsic, E. D · 2020
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Mars: Markov molecular sampling for multi-objective drug discovery
Xie, Y., Shi, C., Zhou, H., Yang, Y., Zhang, W., Yu, Y., and Li, L · 2020
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Molecular mechanics-driven graph neural network with multiplex graph for molecular structures
Zhang, S., Liu, Y., and Xie, L · 2020
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Flow network based generative models for non-iterative diverse candidate generation
Bengio, E., Jain, M., Korablyov, M., Precup, D., and Bengio, Y · 2021
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Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2021
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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.
Neural topological ordering for computation graphs
Gagrani, M., Rainone, C., Yang, Y., Teague, H., Jeon, W., Bondesan, R., van Hoof, H., Lott, C., Zeng, W., and Zappi, P · 2022
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Qiu, Z.-H., Hu, Q., Yuan, Z., Zhou, D., Zhang, L., and Yang, T · 2023
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Thompson sampling for improved exploration in gflownets
Rector-Brooks, J., Madan, K., Jain, M., Korablyov, M., Liu, C.-H., Chandar, S., Malkin, N., and Bengio, Y · 2023
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Towards understanding and improving gflownet training
Shen, M. W., Bengio, E., Hajiramezanali, E., Loukas, A., Cho, K., and Biancalani, T · 2023
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Let the flows tell: Solving graph combinatorial optimization problems with gflownets
Zhang, D., Dai, H., Malkin, N., Courville, A., Bengio, Y., and Pan, L · 2023
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Phylogfn: Phylogenetic inference with generative flow networks
Zhou, M., Yan, Z., Layne, E., Malkin, N., Zhang, D., Jain, M., Blanchette, M., and Bengio, Y · 2023
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Biological sequence design with gflownets
Jain, M., Bengio, E., Hernandez-Garcia, A., Rector-Brooks, J., Dossou, B. F., Ekbote, C. A., Fu, J., Zhang, T., Kilgour, M., Zhang, D., et al · 2022
Cited alongside, same era.
Design-bench: Benchmarks for data-driven offline model-based optimization
Trabucco, B., Geng, X., Kumar, A., and Levine, S · 2022
Cited alongside, same era.
Gflownet foundations
Bengio, Y., Lahlou, S., Deleu, T., Hu, E. J., Tiwari, M., and Bengio, E · 2023
Cited alongside, same era.
Joint bayesian inference of graphical structure and parameters with a single generative flow network
Deleu, T., Nishikawa-Toomey, M., Subramanian, J., Malkin, N., Charlin, L., and Bengio, Y · 2023
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Bootstrapped training of score-conditioned generator for offline design of biological sequences
Kim, M., Berto, F., Ahn, S., and Park, J · 2023
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A theory of continuous generative flow networks
Lahlou, S., Deleu, T., Lemos, P., Zhang, D., Volokhova, A., Hernández-Garcıa, A., Ezzine, L. N., Bengio, Y., and Malkin, N · 2023
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Learning gflownets from partial episodes for improved convergence and stability
Madan, K., Rector-Brooks, J., Korablyov, M., Bengio, E., Jain, M., Nica, A. C., Bosc, T., Bengio, Y., and Malkin, N · 2023
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Delta-AI: Local objectives for amortized inference in sparse graphical models
Falet, J.-P. R., Lee, H. B., Malkin, N., Sun, C., Secrieru, D., Zhang, D., Lajoie, G., and Bengio, Y · 2024
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Amortizing intractable inference in large language models
Hu, E. J., Jain, M., Elmoznino, E., Kaddar, Y., Lajoie, G., Bengio, Y., and Malkin, N · 2024
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Learning energy decompositions for partial inference in GFlownets
Jang, H., Kim, M., and Ahn, S · 2024
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Local search GFlownets
Kim, M., Yun, T., Bengio, E., Zhang, D., Bengio, Y., Ahn, S., and Park, J · 2024
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To cool or not to cool? temperature network meets large foundation models via dro
Qiu, Z.-H., Guo, S., Xu, M., Zhao, T., Zhang, L., and Yang, T · 2024
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Generative flow networks as entropy-regularized rl
Tiapkin, D., Morozov, N., Naumov, A., and Vetrov, D. P · 2024
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