3 papers
cs.LG2025
Softly Symbolifying Kolmogorov-Arnold Networks
James Bagrow, Josh Bongard
Kolmogorov-Arnold Networks (KANs) offer a promising path toward interpretable machine learning: their learnable activations can be studied individually, while collectively fitting…
cs.LG2025
Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony
James Bagrow, Josh Bongard
Kolmogorov-Arnold Networks (KANs) uniquely combine high accuracy with interpretability, making them valuable for scientific modeling. However, it is unclear a priori how deep a net…
cs.NE2025
[RETRACTED]Evolving Form and Function: Dual-Objective Optimization in Neural Symbolic Regression Networks
Amanda Bertschinger, James Bagrow, Joshua Bongard
[RETRACTED]Data increasingly abounds, but distilling their underlying relationships down to something interpretable remains challenging. One approach is genetic programming, which…