Tighter Sparse Approximation Bounds for ReLU Neural Networks
Authors: Carles Domingo-Enrich, Youssef Mroueh
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we extend the framework of (Ongie et al., 2019) and define similar Radon-based semi-norms (R, U-norms) such that a function admits an infinite-width neural network representation on a bounded open set U Rd when its R, U-norm is finite. Building on this, we derive sparse (finite-width) neural network approximation bounds that refine those of Breiman (1993); Klusowski & Barron (2018). Finally, we show that infinite-width neural network representations on bounded open sets are not unique and study their structure, providing a functional view of mode connectivity. |
| Researcher Affiliation | Academia | Anonymous authors Paper under double-blind review |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use datasets for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments or dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments or provide specific hardware details. |
| Software Dependencies | No | The paper is theoretical and does not describe experiments or provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe experiments or provide specific experimental setup details. |