Optimal approximation using complex-valued neural networks
Authors: Paul Geuchen, Felix Voigtlaender
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We thus analyze the expressivity of CVNNs by studying their approximation properties. Our results yield the first quantitative approximation bounds for CVNNs that apply to a wide class of activation functions including the popular mod Re LU and complex cardioid activation functions. |
| Researcher Affiliation | Academia | Paul Geuchen MIDS, KU Eichstätt-Ingolstadt, Auf der Schanz 49, 85049 Ingolstadt, Germany paul.geuchen@ku.de; Felix Voigtlaender MIDS, KU Eichstätt-Ingolstadt, Auf der Schanz 49, 85049 Ingolstadt, Germany felix.voigtlaender@ku.de |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. It focuses on mathematical proofs and theoretical analysis. |
| Open Source Code | No | The paper does not provide any statements about releasing open-source code or links to a code repository. |
| Open Datasets | No | This is a theoretical paper and does not use datasets for training. |
| Dataset Splits | No | This is a theoretical paper and does not involve empirical validation splits for datasets. |
| Hardware Specification | No | This is a theoretical paper and does not describe hardware used for experiments. |
| Software Dependencies | No | This is a theoretical paper and does not list software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | This is a theoretical paper and does not provide details on an experimental setup, hyperparameters, or training configurations. |