Learning Energy-Based Models from Stochastic Interpolants using Spatiotemporal Differences
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914604501696512 |
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| author | Yu, Hanlin OuYang, RuiKang Kaushik, Partha Klami, Arto Gutmann, Michael U. Chehab, Omar |
| author_facet | Yu, Hanlin OuYang, RuiKang Kaushik, Partha Klami, Arto Gutmann, Michael U. Chehab, Omar |
| contents | Learning an energy-based model from data samples is a central problem in machine learning. Many recent and popular methods, such as denoising score matching for training energy-based diffusion models, use stochastic interpolants to corrupt data samples at different noise levels indexed by a time variable. This defines a joint density over both the data space and time, and most methods learn its energy through either spatial or temporal differences. We identify distinct failure modes for both of these approaches. To solve them, we propose Spatiotemporal Noise-Contrastive Estimation (stNCE), a framework for learning the energy through joint spatiotemporal differences. stNCE unifies many existing methods and leads to new training objectives. Experiments on images and molecules demonstrate performance competitive with state-of-the-art density estimation methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_26850 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning Energy-Based Models from Stochastic Interpolants using Spatiotemporal Differences Yu, Hanlin OuYang, RuiKang Kaushik, Partha Klami, Arto Gutmann, Michael U. Chehab, Omar Machine Learning Learning an energy-based model from data samples is a central problem in machine learning. Many recent and popular methods, such as denoising score matching for training energy-based diffusion models, use stochastic interpolants to corrupt data samples at different noise levels indexed by a time variable. This defines a joint density over both the data space and time, and most methods learn its energy through either spatial or temporal differences. We identify distinct failure modes for both of these approaches. To solve them, we propose Spatiotemporal Noise-Contrastive Estimation (stNCE), a framework for learning the energy through joint spatiotemporal differences. stNCE unifies many existing methods and leads to new training objectives. Experiments on images and molecules demonstrate performance competitive with state-of-the-art density estimation methods. |
| title | Learning Energy-Based Models from Stochastic Interpolants using Spatiotemporal Differences |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.26850 |