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Evaluating the robustness of ML models to out-of-distribution data through similarity analysis

author
Lindén, Joakim
Forsberg, Håkan
Daneshtalab, Masoud
Söderquist, Ingemar
statement of authorship
Joakim Lindén, Håkan Forsberg, Masoud Daneshtalab & Ingemar Söderquist
source
New Trends in Database and Information Systems; ADBIS 2023 Short Papers, Doctoral Consortium and Workshops: AIDMA, DOING, K-Gals, MADEISD, PeRS, Barcelona, Spain, September 4–7, 2023, Proceedings
location of publication
Cham
publisher
year of publication
pages
p. 348 - 359
series
conference name, date
27th European Conference on Advances in Databases and Information Systems, ADBIS 2023, 4-7 September 2023
conference location
Barcelona, Spain
url
ISSN
1865-0929
ISBN
978-303142940-8
notes
Bibliogr.: 20 ref
scientific publication
teaduspublikatsioon
TalTech department
language
inglise
subject term
keyword
accuracy estimation
datasets
neural networks
similarity metrics
Lindén, J., Forsberg, H., Daneshtalab, M., Söderquist, I. Evaluating the robustness of ML models to out-of-distribution data through similarity analysis // New Trends in Database and Information Systems; ADBIS 2023 Short Papers, Doctoral Consortium and Workshops: AIDMA, DOING, K-Gals, MADEISD, PeRS, Barcelona, Spain, September 4–7, 2023, Proceedings. Cham : Springer Nature, 2023. p. 348 - 359. (Communications in computer and information science ; 1850). https://doi.org/10.1007/978-3-031-42941-5_30