DeepPySR adds dynamic pruning, exponential Pareto selection, and hierarchical layers to PySR and reports better R²/F1 plus domain-aligned formulas on Feynman and seven real-world datasets.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.Nature Machine Intelligence, 1(5):206–215
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ShifaMind achieves competitive performance with the LAAT baseline on MIMIC-IV top-50 ICD-10 coding while outperforming vanilla concept bottleneck models and providing concept-mediated explanations.
A review paper arguing that accounts of explanation from philosophy of science supply necessary conditions for adequate XAI design in clinical settings.
citing papers explorer
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DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery
DeepPySR adds dynamic pruning, exponential Pareto selection, and hierarchical layers to PySR and reports better R²/F1 plus domain-aligned formulas on Feynman and seven real-world datasets.
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ShifaMind: A Multiplicative Concept Bottleneck for Interpretable ICD-10 Coding
ShifaMind achieves competitive performance with the LAAT baseline on MIMIC-IV top-50 ICD-10 coding while outperforming vanilla concept bottleneck models and providing concept-mediated explanations.
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Scientific Explanations in Health Sciences: Causality, Trust, and Epistemic Adequacy
A review paper arguing that accounts of explanation from philosophy of science supply necessary conditions for adequate XAI design in clinical settings.