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..
Characterizing ResNet’s Universal Approximation Capability
Authors: Chenghao Liu, Enming Liang, Minghua Chen
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we provide function approximation results to numerically validate the theoretical results presented in Sec. 4. To emphasize the approximation error, we involve a sufficiently complex target function for the experiment. Specifically, we utilize the following set of functions (where ai, bi are parameters) to test the universal approximation capability of b-Res Net. ... The results are shown in Figure 2 and Table 4. |
| Researcher Affiliation | Academia | 1School of Data Science, City University of Hong Kong. |
| Pseudocode | No | The paper includes high-level steps for construction (Table 2) and detailed mathematical proofs, but no formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing open-source code or links to code repositories. |
| Open Datasets | No | Specifically, we utilize the following set of functions (where ai, bi are parameters) to test the universal approximation capability of b-Res Net. ... For each case of d = 100, 200, 300, we randomly selected 30 functions from the set for function approximation experiments. |
| Dataset Splits | No | We conduct uniform sampling with 1000 d samples and use 90% for training and 10% for testing, and then take the average loss. |
| Hardware Specification | No | The paper does not specify any hardware details like GPU/CPU models or specific compute resources used for the experiments. |
| Software Dependencies | No | We optimize the network parameters using Adam (Kingma & Ba, 2014) with a learning rate of 10 3. The paper mentions the Adam optimizer but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | Specifically, for each case of d = 100, 200, 300, we randomly selected 30 functions from the set for function approximation experiments. ... We then compare b-Res Net with fully-connected (FC) NN for approximating each sampled function, with network structure as RN (d + 1, n, d/10) for n {10, 20, 40}, and NN (d + 1, d/10), respectively. ... We conduct uniform sampling with 1000 d samples and use 90% for training and 10% for testing, and then take the average loss. We optimize the network parameters using Adam (Kingma & Ba, 2014) with a learning rate of 10 3 and present the test performance over iteration. |