Tag: symbolic
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Neural Networks Are Blurry, Symbolic Systems Are Fragmented. Sparse Autoencoders Help Us Combine Them.
Neural Networks Are Blurry, Symbolic Systems Are Fragmented. Sparse Autoencoders Help Us Combine Them. Neural and symbolic models compress the world in fundamentally different ways, and Sparse Autoencoders (SAEs) offer a bridge to connect them. The post Neural Networks Are Blurry, Symbolic Systems Are Fragmented. Sparse Autoencoders Help Us Combine Them. appeared first on Towards…
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Discovering equations from data: symbolic regression in dynamical systems
Discovering equations from data: symbolic regression in dynamical systems arXiv:2508.20257v1 Announce Type: cross Abstract: The process of discovering equations from data lies at the heart of physics and in many other areas of research, including mathematical ecology and epidemiology. Recently, machine learning methods known as symbolic regression have automated this process. As several methods are…
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Bayesian symbolic regression: Automated equation discovery from a physicists’ perspective
Bayesian symbolic regression: Automated equation discovery from a physicists’ perspective arXiv:2507.19540v1 Announce Type: new Abstract: Symbolic regression automates the process of learning closed-form mathematical models from data. Standard approaches to symbolic regression, as well as newer deep learning approaches, rely on heuristic model selection criteria, heuristic regularization, and heuristic exploration of model space. Here, we…