Author of the publication

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Anomaly Detection in Univariate Time Series: An Empirical Comparison of Machine Learning Algorithms.

S. Däubener, S. Schmitt, H. Wang, P. Krause, and T. Bäck. ICDM, page 161-175. ibai Publishing, (2019)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Walter Däubener

Other publications of authors with the same name

Investigating maximum likelihood based training of infinite mixtures for uncertainty quantification.S. Däubener, and A. Fischer. CoRR, (2020)Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification.S. Däubener, L. Schönherr, A. Fischer, and D. Kolossa. INTERSPEECH, page 4661-4665. ISCA, (2020)Anomaly Detection in Electrocardiogram Readings with Stacked LSTM Networks.M. Thill, S. Däubener, W. Konen, and T. Bäck. ITAT, volume 2473 of CEUR Workshop Proceedings, page 17-25. CEUR-WS.org, (2019)How Sampling Impacts the Robustness of Stochastic Neural Networks.S. Däubener, and A. Fischer. NeurIPS, (2022)Approaches to Uncertainty Quantification in Federated Deep Learning.F. Linsner, L. Adilova, S. Däubener, M. Kamp, and A. Fischer. PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, page 128-145. Springer, (2021)On the Challenges and Opportunities in Generative AI.L. Manduchi, K. Pandey, R. Bamler, R. Cotterell, S. Däubener, S. Fellenz, A. Fischer, T. Gärtner, M. Kirchler, M. Kloft and 15 other author(s). CoRR, (2024)Detecting Compositionally Out-of-Distribution Examples in Semantic Parsing.D. Lukovnikov, S. Däubener, and A. Fischer. EMNLP (Findings), page 591-598. Association for Computational Linguistics, (2021)On the Limitations of Model Stealing with Uncertainty Quantification Models.D. Pape, S. Däubener, T. Eisenhofer, A. Cinà, and L. Schönherr. CoRR, (2023)SmoothLRP: Smoothing LRP by Averaging over Stochastic Input Variations.A. Raulf, S. Däubener, B. Hack, A. Mosig, and A. Fischer. ESANN, (2021)Anomaly Detection in Univariate Time Series: An Empirical Comparison of Machine Learning Algorithms.S. Däubener, S. Schmitt, H. Wang, P. Krause, and T. Bäck. ICDM, page 161-175. ibai Publishing, (2019)

BibSonomy

Disambiguation of "Däubener, Sina"

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Anomaly Detection in Univariate Time Series: An Empirical Comparison of Machine Learning Algorithms.

Please choose a person to relate this publication to

Walter Däubener

Walter Däubener

Walter Däubener

Walter Däubener

Walter Däubener

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Däubener, Sina"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Anomaly Detection in Univariate Time Series: An Empirical Comparison of Machine Learning Algorithms.

Please choose a person to relate this publication to

Walter Däubener

Walter Däubener

Walter Däubener

Walter Däubener

Walter Däubener

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Anomaly Detection in Univariate Time Series: An Empirical Comparison of Machine Learning Algorithms.