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Constructive Bounds for the Topological Expressivity of Neural Networks

Paper recorded by Signals 4 on 2026-10-09 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-09 on arXiv · recorded by Signals 4 on 2026-10-12

Category: cs.LG · 机器学习 · first seen 2026-10-12

Abstract

The expressivity of a neural network $Φ$ describes the class of functions that a network can express. The term topological expressivity refers to a family of measures particularly suited to understand binary classification and is often formulated in terms of the Betti numbers of a decision boundary $β(Φ^{-1}(0))$. Despite its popularity, this measure is not as useful for regression and other probl

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#297 most recent of 749 cs.LG papers we have recorded · ↑ newer: A Rate Transfer Method: Convergence Rates in Stochastic Gradient Desce · ↓ older: AI Models Can Provably Hide Arbitrary Capabilities
Cite this page: Constructive Bounds for the Topological Expressivity of Neural Networks: the #297 most recent of 749 cs.LG papers we have recorded (as of 2026-10-09). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/constructive-bounds-for-the-topological-expressivity-of-neural-networks.html
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