Corrected empirical limits show the most massive galaxies never exceed the theoretical baryonic maximum of 0.16 times halo virial mass, keeping observations consistent with LambdaCDM at all redshifts.
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Adds a trainable feature selection layer to NAM and NBM to cut computational cost, enable two-input interaction networks in high dimensions, and match or exceed state-of-the-art GAM performance.
Strict statistical analysis of 51 tabular datasets finds that meta-features do not reliably explain or predict performance gaps between neural networks, trees, foundation models, and specific variants after multiple-testing correction.
citing papers explorer
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Empirical estimates of how massive galaxies can be in {\Lambda}CDM
Corrected empirical limits show the most massive galaxies never exceed the theoretical baryonic maximum of 0.16 times halo virial mass, keeping observations consistent with LambdaCDM at all redshifts.
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Neural Additive and Basis Models with Feature Selection and Interactions
Adds a trainable feature selection layer to NAM and NBM to cut computational cost, enable two-input interaction networks in high dimensions, and match or exceed state-of-the-art GAM performance.
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Revisiting Metafeatures to Explain Model Differences on Tabular Data
Strict statistical analysis of 51 tabular datasets finds that meta-features do not reliably explain or predict performance gaps between neural networks, trees, foundation models, and specific variants after multiple-testing correction.