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..
The phase diagram of approximation rates for deep neural networks
Authors: Dmitry Yarotsky, Anton Zhevnerchuk
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We explore the phase diagram of approximation rates for deep neural networks and prove several new theoretical results. In the present paper we perform a systematic theoretical study of this question in the context of network expressiveness. |
| Researcher Affiliation | Academia | Dmitry Yarotsky Skolkovo Institute of Science and Technology EMAIL Anton Zhevnerchuk Skolkovo Institute of Science and Technology EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper and does not mention the use of any datasets for training or provide access information for a publicly available dataset. |
| Dataset Splits | No | This is a theoretical paper and does not provide any specific dataset split information (e.g., train/validation/test percentages or counts) needed to reproduce data partitioning. |
| Hardware Specification | No | This is a theoretical paper and does not provide any specific hardware details used for running experiments. |
| Software Dependencies | No | This is a theoretical paper and does not provide any specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate experiments. |
| Experiment Setup | No | This is a theoretical paper and does not contain any specific experimental setup details (e.g., hyperparameters, training configurations, or system-level settings). |