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Generative diffusion models have achieved spectacular performance in many areas of machine learning and generative modeling.
Reverse-time diffusion equation models
Anderson, B. D. (1982) · 1982
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Neural networks and physical systems with emergent collective computational abilities
Hopfield, J. J. (1982) · 1982
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Information capacity of the hopfield model
Abu-Mostafa, Y. and Jacques, J. S. (1985) · 1985
Earlier work this paper cites.
Spin glass theory and beyond: An Introduction to the Replica Method and Its Applications
Mézard, M., Parisi, G., and Virasoro, M. A. (1987) · 1987
Earlier work this paper cites.
Phase transitions in dilute, locally connected neural networks
Strandburg, K. J., Peshkin, M. A., Boyd, D. F., Chambers, C., and O’Keefe, B. (1992) · 1992
Earlier work this paper cites.
On the phase transition of hopfield networks—another monte carlo study
Volk, D. (1998) · 1998
Earlier work this paper cites.
A tutorial on energy-based learning
LeCun, Y., Chopra, S., Hadsell, R., Ranzato, M., and Huang, F. (2006) · 2006
Earlier work this paper cites.
Wavegrad: Estimating gradients for waveform generation
Chen, N., Zhang, Y., Zen, H., Weiss, R. J., Norouzi, M., and Chan, W. (2020) · 2009
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Diffwave: A versatile diffusion model for audio synthesis
Kong, Z., Ping, W., Huang, J., Zhao, K., and Catanzaro, B. (2020) · 2009
Earlier work this paper cites.
The free-energy principle: a unified brain theory?
Friston, K. (2010) · 2010
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S. (2015) · 2015
Earlier work this paper cites.
Dense associative memory for pattern recognition
Krotov, D. and Hopfield, J. J. (2016) · 2016
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On a model of associative memory with huge storage capacity
Demircigil, M., Heusel, J., Löwe, M., Upgang, S., and Vermet, F. (2017) · 2017
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P. (2020) · 2020
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Boltzmann machines as generalized hopfield networks: a review of recent results and outlooks
Marullo, C. and Agliari, E. (2020) · 2020
Cited alongside, same era.
Deep generative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models
Bond-Taylor, S., Leach, A., Long, Y., and Willcocks, C. G. (2021) · 2021
Cited alongside, same era.
Hopfield networks is all you need
Ramsauer, H., Schäfl, B., Lehner, J., Seidl, P., Widrich, M., Adler, T., Gruber, L., Holzleitner, M., Pavlović, M., Sandve, G. K., et al. (2021) · 2021
Cited alongside, same era.
Sampling from the sherrington-kirkpatrick gibbs measure via algorithmic stochastic localization
Hoover, B., Strobelt, H., Krotov, D., Hoffman, J., Kira, Z., and Chau, H. (2023) · 2023
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A new frontier for hopfield networks
Krotov, D. (2023) · 2023
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Audioldm: Text-to-audio generation with latent diffusion models
Liu, H., Chen, Z., Yuan, Y., Mei, X., Liu, X., Mandic, D., Wang, W., and Plumbley, M. D. (2023) · 2023
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Sampling, diffusions, and stochastic localization
Montanari, A. (2023) · 2023
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Spontaneous symmetry breaking in generative diffusion models
Raya, G. and Ambrogioni, L. (2023) · 2023
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El A., A., Montanari, A., and Sellke, M. (2022) · 2022
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Ho, J., Salimans, T., Gritsenko, A., Chan, W., Norouzi, M., and Fleet, D. J. (2022) · 2022
Cited alongside, same era.
Make-a-video: Text-to-video generation without text-video data
Singer, U., Polyak, A., Hayes, T., Yin, X., An, J., Zhang, S., Hu, Q., Yang, H., Ashual, O., Gafni, O., et al. (2022) · 2022
Cited alongside, same era.
Sampling from mean-field gibbs measures via diffusion processes
Alaoui, A. E., Montanari, A., and Sellke, M. (2023) · 2023
Cited alongside, same era.
In search of dispersed memories: Generative diffusion models are associative memory networks
Ambrogioni, L. (2023) · 2023
Cited alongside, same era.
Generative diffusion in very large dimensions
Biroli, G. and Mézard, M. (2023) · 2023
Cited alongside, same era.
Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S. (2021a)
Cited in the paper.
Nearly d-linear convergence bounds for diffusion models via stochastic localization
Benton, J., De Bortoli, V., Doucet, A., and Deligiannidis, G. (2024) · 2024
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Dynamical regimes of diffusion models
Biroli, G., Bonnaire, T., de Bortoli, V., and Mézard, M. (2024) · 2024
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Sampling from spherical spin glasses in total variation via algorithmic stochastic localization
Huang, B., Montanari, A., and Pham, H. T. (2024) · 2024
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Critical windows: non-asymptotic theory for feature emergence in diffusion models
Li, M. and Chen, S. (2024) · 2024
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Exponential capacity of dense associative memories
Lucibello, C. and Mézard, M. (2024) · 2024
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A phase transition in diffusion models reveals the hierarchical nature of data
Sclocchi, A., Favero, A., and Wyart, M. (2024) · 2024
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