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There are many frameworks for deep generative modeling, each often presented with their own specific training algorithms and inference methods.
Sur la théorie relativiste de l’électron et l’interprétation de la mécanique quantique
Schrödinger, E · 1932
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Pulse code communication
Gray, F · 1953
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Probability densities with given marginals
Kullback, S · 1968
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Reverse-time diffusion equation models
Anderson, B. D. O · 1982
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Stochastic differential equations
Øksendal, B · 1985
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The helmholtz machine
Dayan, P., Hinton, G. E., Neal, R. M., and Zemel, R. S · 1995
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The "wake-sleep" algorithm for unsupervised neural networks
Hinton, G. E., Dayan, P., Frey, B. J., and Neal, R. M · 1995
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Modeling high-dimensional discrete data with multi-layer neural networks
Bengio, Y. and Bengio, S · 1999
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Latent dirichlet allocation
Blei, D. M., Ng, A., and Jordan, M. I · 2001
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Training products of experts by minimizing contrastive divergence
Hinton, G. E · 2002
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Self-improving reactive agents based on reinforcement learning, planning and teaching
Lin, L · 2004
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Reinforcement learning: An introduction
Sutton, R. S. and Barto, A. G · 2005
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A tutorial on energy-based learning
LeCun, Y., Chopra, S., Hadsell, R., Ranzato, A., and Huang, F. J · 2006
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Training restricted Boltzmann machines using approximations to the likelihood gradient
Tieleman, T · 2008
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Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P.-A · 2008
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Probabilistic graphical models: principles and techniques
Koller, D. and Friedman, N · 2009
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A connection between score matching and denoising autoencoders
Vincent, P · 2011
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A kernel two-sample test
Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A · 2012
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Batch reinforcement learning
Lange, S., Gabel, T., and Riedmiller, M. A · 2012
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Machine learning: a probabilistic perspective
Murphy, K. P · 2012
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Auto-encoding variational Bayes
Kingma, D. P. and Welling, M · 2013
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A survey of the Schrödinger problem and some of its connections with optimal transport
Léonard, C · 2013
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NICE: Non-linear independent components estimation
Dinh, L., Krueger, D., and Bengio, Y · 2014
Cited alongside, same era.
Generative adversarial nets
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. C., and Bengio, Y · 2014
Cited alongside, same era.
Nice: Non-linear independent components estimation
Dinh, L., Krueger, D., and Bengio, Y · 2015
Cited alongside, same era.
Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J. N., Weiss, E. A., Maheswaranathan, N., and Ganguli, S · 2015
Cited alongside, same era.
Importance weighted autoencoders
Burda, Y., Grosse, R. B., and Salakhutdinov, R · 2016
Cited alongside, same era.
Structured denoising diffusion models in discrete state-spaces
Austin, J., Johnson, D. D., Ho, J., Tarlow, D., and van den Berg, R · 2021
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Diffusion schrödinger bridge with applications to score-based generative modeling
Bortoli, V. D., Thornton, J., Heng, J., and Doucet, A · 2021
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Optimal transport in systems and control
Chen, Y., Georgiou, T. T., and Pavon, M · 2021
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Very deep VAEs generalize autoregressive models and can outperform them on images
Child, R · 2021
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Diffusion models beat GANs on image synthesis
Dhariwal, P. and Nichol, A · 2021
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Argmax flows and multinomial diffusion: Learning categorical distributions
Hoogeboom, E., Nielsen, D., Jaini, P., Forr’e, P., and Welling, M · 2021
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Hierarchical variational models
Ranganath, R., Tran, D., and Blei, D. M · 2016
Cited alongside, same era.
Ladder variational autoencoders
Sønderby, C. K., Raiko, T., Maaløe, L., Sønderby, S. K., and Winther, O · 2016
Cited alongside, same era.
Pixel recurrent neural networks
van den Oord, A., Kalchbrenner, N., and Kavukcuoglu, K · 2016
Cited alongside, same era.
Deep reinforcement learning that matters
Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., and Meger, D · 2017
Cited alongside, same era.
GANs trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
Cited alongside, same era.
Non-autoregressive neural machine translation
Gu, J., Bradbury, J., Xiong, C., Li, V. O. K., and Socher, R · 2018
Cited alongside, same era.
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A variational perspective on diffusion-based generative models and score matching
Huang, C.-W., Lim, J. H., and Courville, A. C · 2021
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Variational diffusion models
Kingma, D. P., Salimans, T., Poole, B., and Ho, J · 2021
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Improved denoising diffusion probabilistic models
Nichol, A. and Dhariwal, P · 2021
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Maximum likelihood training of score-based diffusion models
Song, Y., Durkan, C., Murray, I., and Ermon, S · 2021
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Unifying likelihood-free inference with black-box optimization and beyond
Zhang, D., Fu, J., Bengio, Y., and Courville, A. C · 2021
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Bayesian structure learning with generative flow networks
Deleu, T., G’ois, A., Emezue, C. C., Rankawat, M., Lacoste-Julien, S., Bauer, S., and Bengio, Y · 2022
Closest in time.
GFlowOut: Dropout with generative flow networks
Liu, D., Jain, M., Dossou, B. F. P., Shen, Q., Lahlou, S., Goyal, A., Malkin, N., Emezue, C. C., Zhang, D., Hassen, N., Ji, X., Kawaguchi, K., and Bengio, Y · 2022
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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 · 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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Generative augmented flow networks
Pan, L., Zhang, D., Courville, A. C., Huang, L., and Bengio, Y · 2022
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Conditional simulation using diffusion schrödinger bridges
Shi, Y., Bortoli, V. D., Deligiannidis, G., and Doucet, A · 2022
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Bit prioritization in variational autoencoders via progressive coding
Shu, R. and Ermon, S · 2022
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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
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GFlowNets and variational inference
Malkin, N., Lahlou, S., Deleu, T., Ji, X., Hu, E., Everett, K., Zhang, D., and Bengio, Y · 2023
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