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.
Training Neural Networks is NP-Hard in Fixed Dimension
Authors: Vincent Froese, Christoph Hertrich
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 15,957 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,465 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 | 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. |