Paper recorded by Signals 4 on 2026-10-22 in cs.LG. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-22 on arXiv · recorded by Signals 4 on 2026-10-23
Category: cs.LG · 机器学习 · first seen 2026-10-23
Constructing accurate and computationally tractable approximation spaces is a fundamental task in scientific computing. Partially trainable neural networks bridge fixed random features and fully adaptive representations. This paper quantifies a phase transition pattern in approximation bounds and observes analogous behavior in training errors: a rapid but smooth initial improvement followed by dim