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..
Training Neural Networks is ER-complete
Authors: Mikkel Abrahamsen, Linda Kleist, Tillmann Miltzow
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We determine the algorithmic complexity of this fundamental problem precisely, by showing that it is R-complete. In this paper, we show that it is R-complete to decide if there exists weights and biases that will result in a cost below a given threshold. |
| Researcher Affiliation | Academia | Mikkel Abrahamsen University of Copenhagen EMAIL Linda Kleist Technische Universtität Braunschweig EMAIL Tillmann Miltzow Utrecht University EMAIL |
| Pseudocode | No | The paper describes the reduction and the construction process using narrative text and figures, but it does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that open-source code for the described methodology is available. Under '3. If you ran experiments...', the authors explicitly state '[N/A]' for providing code. |
| Open Datasets | No | The paper is theoretical and focuses on algorithmic complexity, not empirical evaluation. It defines NN-TRAINING with a conceptual 'training data' (D) but does not use or provide access to a specific, publicly available dataset for experiments. Under '3. If you ran experiments...', the authors explicitly state '[N/A]' for data. |
| Dataset Splits | No | The paper does not describe any experiments involving data splits for training, validation, or testing, as it is a theoretical work. Under '3. If you ran experiments...', the authors explicitly state '[N/A]' for specifying all training details. |
| Hardware Specification | No | The paper is theoretical and does not report on empirical experiments that would require specific hardware. Under '3. If you ran experiments...', the authors explicitly state '[N/A]' for including the total amount of compute and the type of resources used. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers as it is a theoretical work and does not report on empirical experiments requiring specific software environments. |
| Experiment Setup | No | The paper is purely theoretical and does not describe any empirical experiments, thus it does not include details about an experimental setup, hyperparameters, or system-level training settings. Under '3. If you ran experiments...', the authors explicitly state '[N/A]' for specifying all training details. |