Learning in Deep Factor Graphs with Gaussian Belief Propagation
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866929423148646400 |
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| author | Nabarro, Seth van der Wilk, Mark Davison, Andrew J |
| author_facet | Nabarro, Seth van der Wilk, Mark Davison, Andrew J |
| contents | We propose an approach to do learning in Gaussian factor graphs. We treat all relevant quantities (inputs, outputs, parameters, latents) as random variables in a graphical model, and view both training and prediction as inference problems with different observed nodes. Our experiments show that these problems can be efficiently solved with belief propagation (BP), whose updates are inherently local, presenting exciting opportunities for distributed and asynchronous training. Our approach can be scaled to deep networks and provides a natural means to do continual learning: use the BP-estimated parameter marginals of the current task as parameter priors for the next. On a video denoising task we demonstrate the benefit of learnable parameters over a classical factor graph approach and we show encouraging performance of deep factor graphs for continual image classification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_14649 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Learning in Deep Factor Graphs with Gaussian Belief Propagation Nabarro, Seth van der Wilk, Mark Davison, Andrew J Machine Learning We propose an approach to do learning in Gaussian factor graphs. We treat all relevant quantities (inputs, outputs, parameters, latents) as random variables in a graphical model, and view both training and prediction as inference problems with different observed nodes. Our experiments show that these problems can be efficiently solved with belief propagation (BP), whose updates are inherently local, presenting exciting opportunities for distributed and asynchronous training. Our approach can be scaled to deep networks and provides a natural means to do continual learning: use the BP-estimated parameter marginals of the current task as parameter priors for the next. On a video denoising task we demonstrate the benefit of learnable parameters over a classical factor graph approach and we show encouraging performance of deep factor graphs for continual image classification. |
| title | Learning in Deep Factor Graphs with Gaussian Belief Propagation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2311.14649 |