Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Tighter Sparse Approximation Bounds for ReLU Neural Networks
Authors: Carles Domingo-Enrich, Youssef Mroueh
ICLR 2022 | Venue PDF | 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. |