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 [1].
Training Neural Networks is NP-Hard in Fixed Dimension
Authors: Vincent Froese, Christoph Hertrich
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the parameterized complexity of training two-layer neural networks...Our results settle the complexity status regarding these parameters almost completely. In this paper, we answer Question 1 negatively by showing NP-hardness for d = 2 in Theorem 1...We prove Theorem 6 with a parameterized reduction from the 2-HYPERPLANE SEPARABILITY problem. |
| Researcher Affiliation | Academia | Vincent Froese Algorithmics and Computational Complexity Faculty IV, TU Berlin Berlin, Germany EMAIL Christoph Hertrich Department of Mathematics London School of Economics and Political Science London, UK EMAIL |
| Pseudocode | No | The paper describes algorithms and reductions in prose but does not include any formally structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about providing open-source code for the described methodology, nor does it include any links to code repositories. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments using specific datasets, thus no information on public dataset availability for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments, so it does not discuss training/validation/test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not report on empirical experiments; therefore, it does not specify any hardware used. |
| Software Dependencies | No | The paper is theoretical and does not describe software implementation details; therefore, it does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on computational complexity; it does not describe the setup of empirical experiments or hyperparameter details. |