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Using statistical physics methods, we study generative diffusion models in the regime where the dimension of space and the number of data are large, and the score function has been trained optimally.
Random-energy model: An exactly solvable model of disordered systems
Bernard Derrida · 1981
Earlier work this paper cites.
Time reversal of diffusions
Ulrich G Haussmann and Etienne Pardoux · 1986
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Marc Mézard, Giorgio Parisi, and Miguel Angel Virasoro · 1987
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A mathematical reformulation of derrida’s rem and grem
David Ruelle · 1987
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Finite size scaling and numerical simulation of statistical systems
Vladimir Privman · 1990
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
High-dimensional data analysis: The curses and blessings of dimensionality
David L Donoho et al · 2000
Earlier work this paper cites.
Advanced mean field methods: Theory and practice
Manfred Opper and David Saad · 2001
Earlier work this paper cites.
Limit theorems for sums of random exponentials
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Earlier work this paper cites.
Estimation of non-normalized statistical models by score matching
Aapo Hyvärinen and Peter Dayan · 2005
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Learning multiple layers of features from tiny images
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Marc Mezard and Andrea Montanari · 2009
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Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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