Zobrazeno 1 - 10
of 7 536
pro vyhledávání: '"Dippel, A"'
Autor:
Ciernik, Laure, Linhardt, Lorenz, Morik, Marco, Dippel, Jonas, Kornblith, Simon, Muttenthaler, Lukas
The Platonic Representation Hypothesis claims that recent foundation models are converging to a shared representation space as a function of their downstream task performance, irrespective of the objectives and data modalities used to train these mod
Externí odkaz:
http://arxiv.org/abs/2411.05561
Deep learning has led to remarkable advancements in computational histopathology, e.g., in diagnostics, biomarker prediction, and outcome prognosis. Yet, the lack of annotated data and the impact of batch effects, e.g., systematic technical data diff
Externí odkaz:
http://arxiv.org/abs/2411.05489
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
Majority Illusion is a phenomenon in social networks wherein the decision by the majority of the network is not the same as one's personal social circle's majority, leading to an incorrect perception of the majority in a large network. In this paper,
Externí odkaz:
http://arxiv.org/abs/2407.20187
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
Autor:
Hense, Julius, Idaji, Mina Jamshidi, Eberle, Oliver, Schnake, Thomas, Dippel, Jonas, Ciernik, Laure, Buchstab, Oliver, Mock, Andreas, Klauschen, Frederick, Müller, Klaus-Robert
Multiple instance learning (MIL) is an effective and widely used approach for weakly supervised machine learning. In histopathology, MIL models have achieved remarkable success in tasks like tumor detection, biomarker prediction, and outcome prognost
Externí odkaz:
http://arxiv.org/abs/2406.04280
Autor:
Oppliger, J., Küspert, J., Dippel, A. -C., Zimmermann, M. v., Gutowski, O., Ren, X., Zhou, X. J., Zhu, Z., Frison, R., Wang, Q., Martinelli, L., Biało, I., Chang, J.
Spectacular quantum phenomena such as superconductivity often emerge in flat-band systems where Coulomb interactions overpower electron kinetics. Engineering strategies for flat-band physics is therefore of great importance. Here, using high-energy g
Externí odkaz:
http://arxiv.org/abs/2404.17795
Autor:
Dippel, Jonas, Feulner, Barbara, Winterhoff, Tobias, Milbich, Timo, Tietz, Stephan, Schallenberg, Simon, Dernbach, Gabriel, Kunft, Andreas, Heinke, Simon, Eich, Marie-Lisa, Ribbat-Idel, Julika, Krupar, Rosemarie, Anders, Philipp, Prenißl, Niklas, Jurmeister, Philipp, Horst, David, Ruff, Lukas, Müller, Klaus-Robert, Klauschen, Frederick, Alber, Maximilian
Artificial intelligence has started to transform histopathology impacting clinical diagnostics and biomedical research. However, while many computational pathology approaches have been proposed, most current AI models are limited with respect to gene
Externí odkaz:
http://arxiv.org/abs/2401.04079
We study the computational complexity of the map redistricting problem (gerrymandering). Mathematically, the electoral district designer (gerrymanderer) attempts to partition a weighted graph into $k$ connected components (districts) such that its ca
Externí odkaz:
http://arxiv.org/abs/2312.14721
Autor:
Dippel, Jack, Vetta, Adrian
In the famous network creation game of Fabrikant et al. a set of agents play a game to build a connected graph. The $n$ agents form the vertex set $V$ of the graph and each vertex $v\in V$ buys a set $E_v$ of edges inducing a graph $G=(V,\bigcup\limi
Externí odkaz:
http://arxiv.org/abs/2310.08663