Zobrazeno 1 - 10
of 4 338
pro vyhledávání: '"Müller Klaus"'
Autor:
Hansmann Martin-Leo, Klauschen Frederick, Samek Wojciech, Müller Klaus-Robert, Donnadieu Emmanuel, Scharf Sonja, Hartmann Sylvia, Koch Ina, Ackermann Jörg, Pantanowitz Liron, Schäfer Hendrik, Wurzel Patrick
Publikováno v:
Journal of Pathology Informatics, Vol 14, Iss , Pp 100298- (2023)
In recent years, medical disciplines have moved closer together and rigid borders have been increasingly dissolved. The synergetic advantage of combining multiple disciplines is particularly important for radiology, nuclear medicine, and pathology to
Externí odkaz:
https://doaj.org/article/ac8621038fe94564b564579214dd5f00
Long-range correlations are essential across numerous machine learning tasks, especially for data embedded in Euclidean space, where the relative positions and orientations of distant components are often critical for accurate predictions. Self-atten
Externí odkaz:
http://arxiv.org/abs/2412.08541
The increasing use of large language models (LLMs) in causal discovery as a substitute for human domain experts highlights the need for optimal model selection. This paper presents the first hallucination survey of popular LLMs for causal discovery.
Externí odkaz:
http://arxiv.org/abs/2411.12759
Autor:
Sextro, Marvin, Dernbach, Gabriel, Standvoss, Kai, Schallenberg, Simon, Klauschen, Frederick, Müller, Klaus-Robert, Alber, Maximilian, Ruff, Lukas
Understanding how deep learning models predict oncology patient risk can provide critical insights into disease progression, support clinical decision-making, and pave the way for trustworthy and data-driven precision medicine. Building on recent adv
Externí odkaz:
http://arxiv.org/abs/2411.07643
Reconstructing brain sources is a fundamental challenge in neuroscience, crucial for understanding brain function and dysfunction. Electroencephalography (EEG) signals have a high temporal resolution. However, identifying the correct spatial location
Externí odkaz:
http://arxiv.org/abs/2411.00143
Causal networks are widely used in many fields to model the complex relationships between variables. A recent approach has sought to construct causal networks by leveraging the wisdom of crowds through the collective participation of humans. While th
Externí odkaz:
http://arxiv.org/abs/2410.14146
Autor:
Esders, Malte, Schnake, Thomas, Lederer, Jonas, Kabylda, Adil, Montavon, Grégoire, Tkatchenko, Alexandre, Müller, Klaus-Robert
While machine learning (ML) models have been able to achieve unprecedented accuracies across various prediction tasks in quantum chemistry, it is now apparent that accuracy on a test set alone is not a guarantee for robust chemical modeling such as s
Externí odkaz:
http://arxiv.org/abs/2410.13833
Autor:
Muttenthaler, Lukas, Greff, Klaus, Born, Frieda, Spitzer, Bernhard, Kornblith, Simon, Mozer, Michael C., Müller, Klaus-Robert, Unterthiner, Thomas, Lampinen, Andrew K.
Deep neural networks have achieved success across a wide range of applications, including as models of human behavior in vision tasks. However, neural network training and human learning differ in fundamental ways, and neural networks often fail to g
Externí odkaz:
http://arxiv.org/abs/2409.06509
Medical imaging applications are highly specialized in terms of human anatomy, pathology, and imaging domains. Therefore, annotated training datasets for training deep learning applications in medical imaging not only need to be highly accurate but a
Externí odkaz:
http://arxiv.org/abs/2409.04670
When modeling physical properties of molecules with machine learning, it is desirable to incorporate $SO(3)$-covariance. While such models based on low body order features are not complete, we formulate and prove general completeness properties for h
Externí odkaz:
http://arxiv.org/abs/2409.02730