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arXiv · 2007.10784

OccamNet: A Fast Neural Model for Symbolic Regression at Scale

Owen Dugan·Rumen Dangovski·Allan Costa·Samuel Kim·Pawan Goyal·Joseph Jacobson·Marin Soljačić
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Abstract

Neural networks' expressiveness comes at the cost of complex, black-box models that often extrapolate poorly beyond the domain of the training dataset, conflicting with the goal of finding compact analytic expressions to describe scientific data. We introduce OccamNet, a neural network model that finds interpretable, compact, and sparse symbolic fits to data, \`a la Occam's razor. Our model defines a probability distribution over functions with efficient sampling and function evaluation. We train by sampling functions and biasing the probability mass toward better fitting solutions, backpropagating using cross-entropy matching in a reinforcement-learning loss. OccamNet can identify symbolic fits for a variety of problems, including analytic and non-analytic functions, implicit functions, and simple image classification, and can outperform state-of-the-art symbolic regression methods on real-world regression datasets. Our method requires a minimal memory footprint, fits complicated functions in minutes on a single CPU, and scales on a GPU.

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Owen Dugan, Rumen Dangovski, Allan Costa, Samuel Kim, Pawan Goyal, Joseph Jacobson, Marin Soljačić. 2020-07-16. OccamNet: A Fast Neural Model for Symbolic Regression at Scale. https://arxiv.org/abs/2007.10784

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