2 papers
cs.LG2026
Interpretability and Generalization Bounds for Learning Spatial Physics
Alejandro Francisco Queiruga, Theo Gutman-Solo, Shuai Jiang
While there are many applications of ML to scientific problems that look promising, visuals can be deceiving. Using numerical analysis techniques, we rigorously quantify the accura…
cs.LG2026
Divine Benevolence is an : GLUs scale asymptotically faster than MLPs
Alejandro Francisco Queiruga
Scaling laws can be understood from ground-up numerical analysis, where traditional function approximation theory can explain shifts in model architecture choices. GLU variants now…