Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Marcinkevičs, Ričards"'
Autor:
Marcinkevičs, Ričards, Wolfertstetter, Patricia Reis, Klimiene, Ugne, Chin-Cheong, Kieran, Paschke, Alyssia, Zerres, Julia, Denzinger, Markus, Niederberger, David, Wellmann, Sven, Ozkan, Ece, Knorr, Christian, Vogt, Julia E.
Appendicitis is among the most frequent reasons for pediatric abdominal surgeries. With recent advances in machine learning, data-driven decision support could help clinicians diagnose and manage patients while reducing the number of non-critical sur
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1973774ea72cfd7169f3d46f6fa550a0
Publikováno v:
The Eleventh International Conference on Learning Representations (ICLR 2023)
Spurious correlations are everywhere. While humans often do not perceive them, neural networks are notorious for learning unwanted associations, also known as biases, instead of the underlying decision rule. As a result, practitioners are often unawa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::059d7fb070f9eac424f6ec6fa57d9b76
Autor:
Manduchi, Laura, Marcinkevičs, Ričards, Massi, Michela C., Weikert, Thomas, Sauter, Alexander, Gotta, Verena, Müller, Timothy, Vasella, Flavio, Neidert, Marian C., Pfister, Marc, Stieltjes, Bram, Vogt, Julia E.
Publikováno v:
International Conference on Learning Representations (ICLR 2022)
In this work, we study the problem of clustering survival data $-$ a challenging and so far under-explored task. We introduce a novel semi-supervised probabilistic approach to cluster survival data by leveraging recent advances in stochastic gradient
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::13734600ebb7b3dd896c77f830c6e009
https://hdl.handle.net/20.500.11850/536597
https://hdl.handle.net/20.500.11850/536597
Publikováno v:
Proceedings of Machine Learning Research, 182
Proceedings of the 7th Machine Learning for Healthcare Conference
Proceedings of the 7th Machine Learning for Healthcare Conference
Deep neural networks for image-based screening and computer-aided diagnosis have achieved expert-level performance on various medical imaging modalities, including chest radiographs. Recently, several works have indicated that these state-of-the-art
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::08df21f4ee3c72ea400f2a52dc9250b0
Many modern research fields increasingly rely on collecting and analysing massive, often unstructured, and unwieldy datasets. Consequently, there is growing interest in machine learning and artificial intelligence applications that can harness this `
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e3401b1a7a5202023e1e5bc283ce2604
Autor:
Marcinkevičs, Ričards, Vogt, Julia E.
In this review, we examine the problem of designing interpretable and explainable machine learning models. Interpretability and explainability lie at the core of many machine learning and statistical applications in medicine, economics, law, and natu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ca2bd9eea866d436b9b3ee64ca4455a2