Zobrazeno 1 - 10 of 33 pro vyhledávání: '"Ulrike von Luxburg"'
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Akademický článek
Group decisions based on confidence weighted majority voting
Autor: Sascha Meyen, Dorothee M. B. Sigg, Ulrike von Luxburg, Volker H. Franz
Publikováno v: Cognitive Research, Vol 6, Iss 1, Pp 1-13 (2021)
Abstract Background It has repeatedly been reported that, when making decisions under uncertainty, groups outperform individuals. Real groups are often replaced by simulated groups: Instead of performing an actual group discussion, individual respons
Externí odkaz: https://doaj.org/article/e4ad22f9b3f44d08bbd0bfa19fa2f14a
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2
Empirical Distortions in Climate Networks
Autor: Moritz Haas, Bedartha Goswami, Ulrike von Luxburg
Climate networks have become a popular tool for detecting complex structures in spatio-temporal data. However, they require to estimate correlation values on many edges based on limited and noisy time series. Consequently any constructed network like
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c6f5e3952308832eedb83e301ae1f184
https://doi.org/10.5194/egusphere-egu23-13601
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3
Estimating the perceived dimension of psychophysical stimuli using triplet accuracy and hypothesis testing
Autor: David-Elias, Künstle, Ulrike, von Luxburg, Felix A, Wichmann
Publikováno v: Journal of vision. 22(13)
Vision researchers are interested in mapping complex physical stimuli to perceptual dimensions. Such a mapping can be constructed using multidimensional psychophysical scaling or ordinal embedding methods. Both methods infer coordinates that agree as
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid________::58a58ba9085739df0e743a15da1ab26f
https://pubmed.ncbi.nlm.nih.gov/36469015
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4
Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts
Autor: Sebastian Bordt, Michèle Finck, Eric Raidl, Ulrike von Luxburg
Existing and planned legislation stipulates various obligations to provide information about machine learning algorithms and their functioning, often interpreted as obligations to "explain". Many researchers suggest using post-hoc explanation algorit
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::133af27af14452662d32e9056be338f1
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6
Explaining the Explainer: A First Theoretical Analysis of LIME
Autor: Damien Garreau, Ulrike von Luxburg
Publikováno v: Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics
AISTATS 2020-23rd International Conference on Artificial Intelligence and Statistics
AISTATS 2020-23rd International Conference on Artificial Intelligence and Statistics, Aug 2020, Palermo /Online, Italy
HAL
Machine learning is used more and more often for sensitive applications, sometimes replacing humans in critical decision-making processes. As such, interpretability of these algorithms is a pressing need. One popular algorithm to provide interpretabi
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::707f7630854ae256c6a40e0d52e324e4
https://hal.archives-ouvertes.fr/hal-03233013v2/document
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7
Advancing Research on Unconscious Priming: When can Scientists Claim an Indirect Task Advantage?
Autor: Ulrike von Luxburg, Catarina Amado, Sascha Meyen, Iris A. Zerweck, Volker H. Franz
Current literature holds that many cognitive functions can be performed outside consciousness. Evidence for this view comes from unconscious priming. In a typical experiment, visual stimuli are masked, such that participants are close to chance when
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8c1ef5cb476eafcba14e0f3e2ba0c9e4
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9
Boosting for Comparison-Based Learning
Autor: Ulrike von Luxburg, Michaël Perrot
Publikováno v: IJCAI
We consider the problem of classification in a comparison-based setting: given a set of objects, we only have access to triplet comparisons of the form ``object A is closer to object B than to object C.'' In this paper we introduce TripletBoost, a ne
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a7cd1343128b527244f1733e5bde3e08
https://doi.org/10.24963/ijcai.2019/255
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10
Feasibility of Active Machine Learning for Multiclass Compound Classification
Autor: Matthias Rarey, Florian Flachsenberg, Ulrike von Luxburg, Tobias Lang
Publikováno v: Journal of Chemical Information and Modeling. 56:12-20
A common task in the hit-to-lead process is classifying sets of compounds into multiple, usually structural classes, which build the groundwork for subsequent SAR studies. Machine learning techniques can be used to automate this process by learning c
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7e9ae0c525a628cab57805e00bb358e8
https://doi.org/10.1021/acs.jcim.5b00332
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