3 papers
cs.LG2026
Neural Global Optimization via Iterative Refinement from Noisy Samples
Qusay Muzaffar, David Levin, Michael Werman
Global optimization of black-box functions from noisy samples is a fundamental challenge in machine learning and scientific computing. Traditional methods such as Bayesian Optimiza…
math.NA2025
Exponential Convergence of Deep Composite Polynomial Approximation for Cusp-Type Functions
Kingsley Yeon, Steven B. Damelin, Michael Werman
We investigate deep composite polynomial approximations of continuous but non-differentiable functions with algebraic cusp singularities. The functions in focus consist of finitely…
cs.CV2025
CanonNet: Canonical Ordering and Curvature Learning for Point Cloud Analysis
Benjy Friedmann, Michael Werman
Point cloud processing poses two fundamental challenges: establishing consistent point ordering and effectively learning fine-grained geometric features. Current architectures rely…