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Found 4 papers, 3 papers with code
Symbolic Regression via Neural-Guided Genetic Programming Population Seeding
Symbolic regression is the process of identifying mathematical expressions that fit observed output from a black-box process.
Incorporating domain knowledge into neural-guided search
Many AutoML problems involve optimizing discrete objects under a black-box reward.
Improving exploration in policy gradient search: Application to symbolic optimization
Many machine learning strategies designed to automate mathematical tasks leverage neural networks to search large combinatorial spaces of mathematical symbols.
Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients
Discovering the underlying mathematical expressions describing a dataset is a core challenge for artificial intelligence.
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