Zobrazeno 1 - 10
of 1 077
pro vyhledávání: '"Schneider, Steffen"'
Autor:
Feistritzer, Hans-Josef, Desch, Steffen, Freund, Anne, Poess, Janine, Zeymer, Uwe, Ouarrak, Taoufik, Schneider, Steffen, de Waha-Thiele, Suzanne, Fuernau, Georg, Eitel, Ingo, Noc, Marko, Stepinska, Janina, Huber, Kurt, Thiele, Holger
Objectives: To analyze the use and prognostic impact of active mechanical circulatory support (MCS) devices in a large prospective contemporary cohort of patients with cardiogenic shock (CS) complicating acute myocardial infarction (AMI). Background:
Externí odkaz:
https://ul.qucosa.de/id/qucosa%3A84801
https://ul.qucosa.de/api/qucosa%3A84801/attachment/ATT-0/
https://ul.qucosa.de/api/qucosa%3A84801/attachment/ATT-0/
Test-Time Adaptation (TTA) allows to update pre-trained models to changing data distributions at deployment time. While early work tested these algorithms for individual fixed distribution shifts, recent work proposed and applied methods for continua
Externí odkaz:
http://arxiv.org/abs/2306.05401
Mapping behavioral actions to neural activity is a fundamental goal of neuroscience. As our ability to record large neural and behavioral data increases, there is growing interest in modeling neural dynamics during adaptive behaviors to probe neural
Externí odkaz:
http://arxiv.org/abs/2204.00673
Autor:
Ye, Shaokai, Filippova, Anastasiia, Lauer, Jessy, Schneider, Steffen, Vidal, Maxime, Qiu, Tian, Mathis, Alexander, Mathis, Mackenzie Weygandt
Quantification of behavior is critical in applications ranging from neuroscience, veterinary medicine and animal conservation efforts. A common key step for behavioral analysis is first extracting relevant keypoints on animals, known as pose estimati
Externí odkaz:
http://arxiv.org/abs/2203.07436
Autor:
Nemec, Stefan F., Schneider, Steffen, Friedrich, Klaus M., Weber, Michael, Schwarz-Nemec, Ursula
Publikováno v:
In Journal of Cranio-Maxillo-Facial Surgery May 2024 52(5):644-651
Autor:
Tangemann, Matthias, Schneider, Steffen, von Kügelgen, Julius, Locatello, Francesco, Gehler, Peter, Brox, Thomas, Kümmerer, Matthias, Bethge, Matthias, Schölkopf, Bernhard
Learning generative object models from unlabelled videos is a long standing problem and required for causal scene modeling. We decompose this problem into three easier subtasks, and provide candidate solutions for each of them. Inspired by the Common
Externí odkaz:
http://arxiv.org/abs/2110.06562
Publikováno v:
In Heliyon 15 April 2024 10(7)