Fetching the paper…
Reading the bibliography…
Generative Flow Networks or GFlowNets are related to Monte-Carlo Markov chain methods (as they sample from a distribution specified by an energy function), reinforcement learning (as they learn a policy to sample composed objects through a sequence of steps), generative models (as they learn to represent and sample from a distribution) and amortized variational methods (as they can be used to learn to approximate and sample from an otherwise intractable posterior, given a prior and a likelihood).
Equation of state calculations by fast computing machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E · 1953
Earlier work this paper cites.
Monte carlo sampling methods using markov chains and their applications
Hastings, W. K · 1970
Earlier work this paper cites.
Working memory
Baddeley, A · 1992
Earlier work this paper cites.
A cognitive theory of consciousness
Baars, B. J · 1993
Earlier work this paper cites.
A neuronal model of a global workspace in effortful cognitive tasks
Dehaene, S., Kerszberg, M., and Changeux, J.-P · 1998
Earlier work this paper cites.
An embedded-processes model of working memory
Cowan, N · 1999
Earlier work this paper cites.
An introduction to mcmc for machine learning
Andrieu, C., De Freitas, N., Doucet, A., and Jordan, M. I · 2003
Earlier work this paper cites.
Applying global workspace theory to the frame problem
Shanahan, M. and Baars, B · 2005
Earlier work this paper cites.
A cognitive architecture that combines internal simulation with a global workspace
Shanahan, M · 2006
Earlier work this paper cites.
Learning in markov random fields using tempered transitions
Salakhutdinov, R · 2009
Earlier work this paper cites.
Embodiment and the inner life: Cognition and Consciousness in the Space of Possible Minds
Shanahan, M · 2010
Earlier work this paper cites.
The brain’s connective core and its role in animal cognition
Shanahan, M · 2012
Earlier work this paper cites.
Better mixing via deep representations
Bengio, Y., Mesnil, G., Dauphin, Y., and Rifai, S · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Cited alongside, same era.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Cited alongside, same era.
Auto-encoding variational Bayes
Kingma, D. P. and Welling, M · 2014
Cited alongside, same era.
Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
Cited alongside, same era.
Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, D · 2015
Reinforcement learning: An introduction
Sutton, R. S. and Barto, A. G · 2018
Later among the works it cites.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Later among the works it cites.
The GFlowNet Tutorial
Bengio, Y., Malkin, N., and Jain, M · 2022
Later among the works it cites.
Bayesian structure learning with generative flow networks
Deleu, T., Góis, A., Emezue, C., Rankawat, M., Lacoste-Julien, S., Bauer, S., and Bengio, Y · 2022
Later among the works it cites.
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
Later among the works it cites.
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
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al · 2015
Cited alongside, same era.
Deep learning
Goodfellow, I., Bengio, Y., and Courville, A · 2016
Cited alongside, same era.
What is consciousness, and could machines have it?
Dehaene, S., Lau, H., and Kouider, S · 2017
Cited alongside, same era.
Reinforcement learning with deep energy-based policies
Haarnoja, T., Tang, H., Abbeel, P., and Levine, S · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Cited alongside, same era.
Addressing function approximation error in actor-critic methods
Fujimoto, S., Hoof, H., and Meger, D · 2018
Cited alongside, same era.
Later among the works it cites.
Generative augmented flow networks
Pan, L., Zhang, D., Courville, A., Huang, L., and Bengio, Y · 2022
Later among the works it cites.
Learning long-term reward redistribution via randomized return decomposition
Ren, Z., Guo, R., Zhou, Y., and Peng, J · 2022
Later among the works it cites.
A variational perspective on generative flow networks
Zimmermann, H., Lindsten, F., van de Meent, J.-W., and Naesseth, C. A · 2022
Later among the works it cites.
GFlowNet-EM for learning compositional latent variable models
Hu, E. J., Malkin, N., Jain, M., Everett, K., Graikos, A., and Bengio, Y · 2023
Closest in time.
A theory of continuous generative flow networks
Lahlou, S., Deleu, T., Lemos, P., Zhang, D., Volokhova, A., Hernández-García, A., Ezzine, L. N., Bengio, Y., and Malkin, N · 2023
Closest in time.
Stochastic generative flow networks
Pan, L., Zhang, D., Jain, M., Huang, L., and Bengio, Y · 2023
Closest in time.
Distributional gflownets with quantile flows
Zhang, D., Pan, L., Chen, R. T., Courville, A., and Bengio, Y · 2023
Closest in time.