Learning in Probabilistic Boolean Networks via Structural Policy Gradients
We revisit Probabilistic Boolean Networks as trainable function approximators. The key obstacle, non-differentiable structural choices (which predictors to read and which Boolean operators to apply), is addressed by casting the PBN’s structure as a stochastic policy whose parameters are optimized wi...
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| Hovedforfatter: | |
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| Format: | Artigo |
| Sprog: | Inglês |
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MDPI AG
2025-11-01
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| Serier: | Entropy |
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| Online adgang: | https://www.mdpi.com/1099-4300/27/11/1150 |
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