Zobrazeno 1 - 10
of 1 395
pro vyhledávání: '"Müller Klaus-Robert"'
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
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
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
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
Autor:
Schnake, Thomas, Jafari, Farnoush Rezaei, Lederer, Jonas, Xiong, Ping, Nakajima, Shinichi, Gugler, Stefan, Montavon, Grégoire, Müller, Klaus-Robert
Explainable Artificial Intelligence (XAI) plays a crucial role in fostering transparency and trust in AI systems, where traditional XAI approaches typically offer one level of abstraction for explanations, often in the form of heatmaps highlighting s
Externí odkaz:
http://arxiv.org/abs/2408.17198
Autor:
Kauffmann, Jacob, Dippel, Jonas, Ruff, Lukas, Samek, Wojciech, Müller, Klaus-Robert, Montavon, Grégoire
Unsupervised learning has become an essential building block of AI systems. The representations it produces, e.g. in foundation models, are critical to a wide variety of downstream applications. It is therefore important to carefully examine unsuperv
Externí odkaz:
http://arxiv.org/abs/2408.08041
Autor:
Semnani, Parastoo, Bogojeski, Mihail, Bley, Florian, Zhang, Zizheng, Wu, Qiong, Kneib, Thomas, Herrmann, Jan, Weisser, Christoph, Patcas, Florina, Müller, Klaus-Robert
The successful application of machine learning (ML) in catalyst design relies on high-quality and diverse data to ensure effective generalization to novel compositions, thereby aiding in catalyst discovery. However, due to complex interactions, catal
Externí odkaz:
http://arxiv.org/abs/2407.18935
Autor:
Bonneau, Klara, Lederer, Jonas, Templeton, Clark, Rosenberger, David, Müller, Klaus-Robert, Clementi, Cecilia
Machine learned potentials are becoming a popular tool to define an effective energy model for complex systems, either incorporating electronic structure effects at the atomistic resolution, or effectively renormalizing part of the atomistic degrees
Externí odkaz:
http://arxiv.org/abs/2407.04526
Autor:
Dippel, Jonas, Prenißl, Niklas, Hense, Julius, Liznerski, Philipp, Winterhoff, Tobias, Schallenberg, Simon, Kloft, Marius, Buchstab, Oliver, Horst, David, Alber, Maximilian, Ruff, Lukas, Müller, Klaus-Robert, Klauschen, Frederick
While previous studies have demonstrated the potential of AI to diagnose diseases in imaging data, clinical implementation is still lagging behind. This is partly because AI models require training with large numbers of examples only available for co
Externí odkaz:
http://arxiv.org/abs/2406.14866