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Koenen-et-al_2024_Interpreting deep neural networks with the package innsight.pdf 4,38MB
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1000 Titel
  • Interpreting Deep Neural Networks with the Package innsight
1000 Autor/in
  1. Koenen, Niklas |
  2. Wright, Marvin N. |
1000 Erscheinungsjahr 2024
1000 LeibnizOpen
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2024-11-30
1000 Erschienen in
1000 Quellenangabe
  • 111(8):1-52
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2024
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.18637/jss.v111.i08 |
1000 Ergänzendes Material
  • https://www.jstatsoft.org/index.php/jss/article/view/v111i08/4639 |
  • https://www.jstatsoft.org/index.php/jss/article/view/v111i08/4640 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • The R package innsight offers a general toolbox for revealing variable-wise interpretations of deep neural networks' predictions with so-called feature attribution methods. Aside from the unified and user-friendly framework, the package stands out in three ways: It is generally the first R package implementing feature attribution methods for neural networks. Secondly, it operates independently of the deep learning library, allowing the interpretation of neural networks from any R package, including keras, torch, neuralnet, and even custom models. Despite its flexibility, innsight benefits internally from the torch package's fast and efficient array calculations, which builds on LibTorch - PyTorch's C++ backend - without a Python dependency. Finally, it offers a variety of visualization tools for tabular, signal, image data, or a combination of these. Additionally, the plots can be rendered interactively using the plotly package.
1000 Sacherschließung
lokal neural networks
lokal explainable artificial intelligence
lokal torch
lokal IML
lokal feature attribution
lokal interpretable machine learning
lokal XAI
lokal R
lokal keras
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://orcid.org/0000-0002-4623-8271|https://orcid.org/0000-0002-8542-6291
1000 Label
1000 Förderer
  1. Deutsche Forschungsgemeinschaft |
1000 Fördernummer
  1. 437611051
1000 Förderprogramm
  1. Emmy Noether Grant
1000 Dateien
  1. Interpreting Deep Neural Networks with the Package innsight
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Deutsche Forschungsgemeinschaft |
    1000 Förderprogramm Emmy Noether Grant
    1000 Fördernummer 437611051
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6510234.rdf
1000 Erstellt am 2025-02-26T12:54:27.722+0100
1000 Erstellt von 266
1000 beschreibt frl:6510234
1000 Bearbeitet von 317
1000 Zuletzt bearbeitet 2025-09-12T15:01:51.055+0200
1000 Objekt bearb. Thu Mar 13 10:32:44 CET 2025
1000 Vgl. frl:6510234
1000 Oai Id
  1. oai:frl.publisso.de:frl:6510234 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
1000 Gegenstand von

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