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..
Real-Valued Backpropagation is Unsuitable for Complex-Valued Neural Networks
Authors: Zhi-Hao Tan, Yi Xie, Yuan Jiang, Zhi-Hua Zhou
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, the experiments validate our theoretical findings numerically. |
| Researcher Affiliation | Academia | Zhi-Hao Tan, Yi Xie, Yuan Jiang, Zhi-Hua Zhou National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China EMAIL |
| Pseudocode | No | The paper provides a conceptual "Definition 1 (Complex Tensor Program)" which describes how complex tensor programs are recursively generated, but it does not present a structured pseudocode block or algorithm. |
| Open Source Code | No | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [No] |
| Open Datasets | Yes | The third experiment investigates the convergence of difference between complex NTKs bĪ(n) t and real NTKs Īr during training as the widths go to infinity on MNIST [Le Cun et al., 1998]. |
| Dataset Splits | No | The paper mentions using a "training set D = (X, Y) (|D| = 128)" from MNIST but does not specify how this dataset was split into training, validation, and test subsets, nor does it mention cross-validation details. |
| Hardware Specification | No | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A] The numerical experiments only aim to verify the theoretical results. |
| Software Dependencies | No | All empirical NTKs of complex networks are calculated based on the Neural Tangents library [Novak et al., 2019]. (No specific version is given for this or any other software component). |
| Experiment Setup | Yes | In NTK initialization, the standard deviations are set as 1 for complex networks and scaled to sqrt(2) for real networks. ... The learning rate Ī· is 0.5 for l = 1 and 0.2 for l = 2. |