DiSciPLE uses LLM-guided evolution to discover interpretable Python programs that predict geospatial quantities, outperforming black-box deep nets on population density and on out-of-distribution generalization.
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DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery
DiSciPLE uses LLM-guided evolution to discover interpretable Python programs that predict geospatial quantities, outperforming black-box deep nets on population density and on out-of-distribution generalization.