Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
A closer look at the approximation capabilities of neural networks
Authors: Kai Fong Ernest Chong
ICLR 2020 | Venue PDF | LLM Run Details | Input Tokens: 27,441 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,816 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we give a direct algebraic proof of the theorem. Furthermore we shall explicitly quantify the number of hidden units required for approximation. |
| Researcher Affiliation | Academia | Kai Fong Ernest Chong Information Systems Technology and Design (ISTD) pillar, Singapore University of Technology and Design, Singapore ernest EMAIL |
| Pseudocode | No | The paper is theoretical and does not present any algorithms or procedures in pseudocode format. |
| Open Source Code | No | The paper is theoretical and does not present any new software or code for release. |
| Open Datasets | No | The paper is theoretical and does not involve training models on datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not involve computational experiments requiring specific hardware. |
| Software Dependencies | No | The paper is theoretical and does not involve computational experiments requiring specific software dependencies. |
| Experiment Setup | No | The paper is theoretical and does not involve an experimental setup. |