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..
Elementary superexpressive activations
Authors: Dmitry Yarotsky
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | First, our existence result (Theorem 3) is of course purely theoretical: though the network is small, a huge approximation complexity is hidden in the very special choice of the network weights. |
| Researcher Affiliation | Academia | 1Skolkovo Institute of Science and Technology, Moscow, Russia. Correspondence to: Dmitry Yarotsky <EMAIL>. |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | This paper is theoretical and does not describe a methodology with associated source code for release. |
| Open Datasets | No | This is a theoretical paper and does not describe experiments using a dataset, hence no information about dataset availability for training. |
| Dataset Splits | No | This is a theoretical paper and does not describe experiments requiring training/validation/test splits. |
| Hardware Specification | No | This is a theoretical paper and does not describe experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | This is a theoretical paper and does not describe experiments, thus no software dependencies are mentioned. |
| Experiment Setup | No | This is a theoretical paper and does not describe any experimental setup or hyperparameters. |