Replication Data for: Solving Approximation Tasks with Greedy Deep Kernel Methods

DOI

This archive contains the Python code and result files corresponding to the paper "Solving Approximation Tasks with Greedy Deep Kernel Methods" and allows to reproduce all numerical experiments.

Software requirements and dependencies are listed in the requirements.txt file. For more details we refer to the README.md file

Identifier
DOI https://doi.org/10.18419/DARUS-5167
Metadata Access https://darus.uni-stuttgart.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18419/DARUS-5167
Provenance
Creator Klink, Marian (ORCID: 0009-0002-8961-553X); Ehring, Tobias ORCID logo; Herkert, Robin ORCID logo; Lautenschlager, Robin ORCID logo; Göddeke, Dominik (ORCID: 0000-0002-1552-497X); Haasdonk, Bernard
Publisher DaRUS
Contributor Klink, Marian
Publication Year 2025
Funding Reference DFG SPP 2311 - 465243391 ; DFG EXC 2075 - 390740016 ; DFG 314733389 ; DFG 540080351
Rights MIT License; info:eu-repo/semantics/openAccess; https://spdx.org/licenses/MIT.html
OpenAccess true
Contact Klink, Marian (University of Stuttgart)
Representation
Resource Type Dataset
Format text/markdown; application/zip
Size 10432; 3333153507
Version 2.0
Discipline Mathematics; Natural Sciences