Local Learning Rules for Out-of-Equilibrium Physical Generative Models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912556416761856 |
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| author | Bösch, Cyrill Roeder, Geoffrey Serra-Garcia, Marc Adams, Ryan P. |
| author_facet | Bösch, Cyrill Roeder, Geoffrey Serra-Garcia, Marc Adams, Ryan P. |
| contents | We show that the out-of-equilibrium driving protocol of score-based generative models (SGMs) can be learned via local learning rules. The gradient with respect to the parameters of the driving protocol is computed directly from force measurements or from observed system dynamics. As a demonstration, we implement an SGM in a network of driven, nonlinear, overdamped oscillators coupled to a thermal bath. We first apply it to the problem of sampling from a mixture of two Gaussians in 2D. Finally, we train a 12x12 oscillator network on the MNIST dataset to generate images of handwritten digits 0 and 1. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_19136 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Local Learning Rules for Out-of-Equilibrium Physical Generative Models Bösch, Cyrill Roeder, Geoffrey Serra-Garcia, Marc Adams, Ryan P. Machine Learning Mesoscale and Nanoscale Physics Emerging Technologies Neural and Evolutionary Computing We show that the out-of-equilibrium driving protocol of score-based generative models (SGMs) can be learned via local learning rules. The gradient with respect to the parameters of the driving protocol is computed directly from force measurements or from observed system dynamics. As a demonstration, we implement an SGM in a network of driven, nonlinear, overdamped oscillators coupled to a thermal bath. We first apply it to the problem of sampling from a mixture of two Gaussians in 2D. Finally, we train a 12x12 oscillator network on the MNIST dataset to generate images of handwritten digits 0 and 1. |
| title | Local Learning Rules for Out-of-Equilibrium Physical Generative Models |
| topic | Machine Learning Mesoscale and Nanoscale Physics Emerging Technologies Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2506.19136 |