Zobrazeno 1 - 10
of 100
pro vyhledávání: '"Scherf, Nico"'
The learning process of a reinforcement learning (RL) agent remains poorly understood beyond the mathematical formulation of its learning algorithm. To address this gap, we introduce attention-oriented metrics (ATOMs) to investigate the development o
Externí odkaz:
http://arxiv.org/abs/2406.14324
In this work, we introduce the concept of Active Representation Learning, a novel class of problems that intertwines exploration and representation learning within partially observable environments. We extend ideas from Active Simultaneous Localizati
Externí odkaz:
http://arxiv.org/abs/2406.03845
Autor:
Williams, Elena, Niehaus, Sebastian, Reinelt, Janis, Merola, Alberto, Mihai, Paul Glad, Villringer, Kersten, Thierbach, Konstantin, Medawar, Evelyn, Lichterfeld, Daniel, Roeder, Ingo, Scherf, Nico, Hernández, Maria del C. Valdés
Machine learning algorithms underpin modern diagnostic-aiding software, which has proved valuable in clinical practice, particularly in radiology. However, inaccuracies, mainly due to the limited availability of clinical samples for training these al
Externí odkaz:
http://arxiv.org/abs/2112.03277
A comparative study of semi- and self-supervised semantic segmentation of biomedical microscopy data
Autor:
Horlava, Nastassya, Mironenko, Alisa, Niehaus, Sebastian, Wagner, Sebastian, Roeder, Ingo, Scherf, Nico
In recent years, Convolutional Neural Networks (CNNs) have become the state-of-the-art method for biomedical image analysis. However, these networks are usually trained in a supervised manner, requiring large amounts of labelled training data. These
Externí odkaz:
http://arxiv.org/abs/2011.08076
Autor:
Kloenne, Marie, Niehaus, Sebastian, Lampe, Leonie, Merola, Alberto, Reinelt, Janis, Roeder, Ingo, Scherf, Nico
Publikováno v:
Scientific Reports 10, 10712 (2020)
Machine Learning has considerably improved medical image analysis in the past years. Although data-driven approaches are intrinsically adaptive and thus, generic, they often do not perform the same way on data from different imaging modalities. In pa
Externí odkaz:
http://arxiv.org/abs/1907.10132
Publikováno v:
In Procedia Computer Science 2019 159:784-793
Autor:
Braune, Max, Scherf, Nico, Heine, Claudia, Sygnecka, Katja, Pillaiyar, Thanigaimalai, Parravicini, Chiara, Heimrich, Bernd, Abbracchio, Maria P., Müller, Christa E., Franke, Heike
Characterization of new pharmacological targets is a promising approach in research of neurorepair mechanisms. The G protein-coupled receptor 17 (GPR17) has recently been proposed as an interesting pharmacological target, e.g., in neuroregenerative p
Externí odkaz:
https://ul.qucosa.de/id/qucosa%3A89187
https://ul.qucosa.de/api/qucosa%3A89187/attachment/ATT-0/
https://ul.qucosa.de/api/qucosa%3A89187/attachment/ATT-0/
Autor:
Morawski, Markus, Kirilina, Evgeniya, Scherf, Nico, Jäger, Carsten, Reimann, Katja, Trampel, Robert, Gavriilidis, Filippos, Geyer, Stefan, Biedermann, Bernd, Arendt, Thomas, Weiskopf, Nikolaus
Publikováno v:
In NeuroImage 15 November 2018 182:417-428
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