A Functional Extension of Semi-Structured Networks
Authors: David Rügamer, Bernard Liew, Zainab Altai, Almond Stöcker
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our numerical experiments demonstrate that this approach accurately recovers underlying signals, enhances predictive performance, and performs favorably compared to competing methods. |
| Researcher Affiliation | Academia | David Rügamer Department of Statistics, LMU Munich Munich Center for Machine Learning (MCML) Munich, Germany david@stat.uni-muenchen.de Bernard X.W. Liew, Zainab Altai School of Sport, Rehabilitation and Exercise Sciences University of Essex Colchester, UK [bl19622,z.altai]@essex.ac.uk Almond Stöcker Institute of Mathematics École Polytechnic Fédéral de Lausanne (EPFL) Lausanne, Switzerland almond.stoecker@epfl.ch |
| Pseudocode | No | No pseudocode or algorithm blocks are present in the paper. |
| Open Source Code | Yes | A prototypical implementation is available as an add-on package of deepregression [51] at https://github.com/ neural-structured-additive-learning/funnel. |
| Open Datasets | Yes | The data analyzed in the first experiment is a collection of three publicly available running datasets [14, 27, 28]. |
| Dataset Splits | No | No explicit validation dataset split is mentioned (only train/test splits are provided). |
| Hardware Specification | Yes | All computations were performed on a user PC with Intel(R) Core(TM) i7-8665U CPU @ 1.90GHz, 8 cores, 16 GB RAM using Python 3.8, R 4.2.1, and Tensor Flow 2.10.0. |
| Software Dependencies | Yes | All computations were performed on a user PC with Intel(R) Core(TM) i7-8665U CPU @ 1.90GHz, 8 cores, 16 GB RAM using Python 3.8, R 4.2.1, and Tensor Flow 2.10.0. |
| Experiment Setup | Yes | In all experiments, we use the Adam optimizer with default hyperparameters. No additional learning rate schedule was used. The batch size, maximum number of epochs and early stopping patience was adjusted depending on the size of the dataset. |