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 [1].
Vocabulary for Universal Approximation: A Linguistic Perspective of Mapping Compositions
Authors: Yongqiang Cai
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this article, we investigate the finite case and constructively prove the existence of a finite vocabulary V = {ϕi : Rd Rd | i = 1, ..., n} with n = O(d2) for the universal approximation. Our proof for Theorem 2.2 is constructive, by considering the flow maps of ODEs. |
| Researcher Affiliation | Academia | School of Mathematical Sciences, Laboratory of Mathematics and Complex Systems, MOE, Beijing Normal University, Beijing, 100875, China. Correspondence to: Yongqiang Cai <EMAIL>. |
| Pseudocode | No | No pseudocode or algorithm blocks are present. The paper describes mathematical constructions and proofs. |
| Open Source Code | No | The paper does not mention providing open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not use or reference any datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details, hyperparameters, or training settings. |