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of 212
pro vyhledávání: '"Gaertner, Thomas"'
We present a logic based interpretable model for learning on graphs and an algorithm to distill this model from a Graph Neural Network (GNN). Recent results have shown connections between the expressivity of GNNs and the two-variable fragment of firs
Externí odkaz:
http://arxiv.org/abs/2406.07126
Autor:
Manduchi, Laura, Pandey, Kushagra, Bamler, Robert, Cotterell, Ryan, Däubener, Sina, Fellenz, Sophie, Fischer, Asja, Gärtner, Thomas, Kirchler, Matthias, Kloft, Marius, Li, Yingzhen, Lippert, Christoph, de Melo, Gerard, Nalisnick, Eric, Ommer, Björn, Ranganath, Rajesh, Rudolph, Maja, Ullrich, Karen, Broeck, Guy Van den, Vogt, Julia E, Wang, Yixin, Wenzel, Florian, Wood, Frank, Mandt, Stephan, Fortuin, Vincent
The field of deep generative modeling has grown rapidly and consistently over the years. With the availability of massive amounts of training data coupled with advances in scalable unsupervised learning paradigms, recent large-scale generative models
Externí odkaz:
http://arxiv.org/abs/2403.00025
N-of-1 trials are randomized multi-crossover trials in single participants with the purpose of investigating the possible effects of one or more treatments. Research in the field of N-of-1 trials has primarily focused on scalar outcomes. However, wit
Externí odkaz:
http://arxiv.org/abs/2309.06455
We investigate novel random graph embeddings that can be computed in expected polynomial time and that are able to distinguish all non-isomorphic graphs in expectation. Previous graph embeddings have limited expressiveness and either cannot distingui
Externí odkaz:
http://arxiv.org/abs/2306.05838
Mixture of experts (MoE), introduced over 20 years ago, is the simplest gated modular neural network architecture. There is renewed interest in MoE because the conditional computation allows only parts of the network to be used during each inference,
Externí odkaz:
http://arxiv.org/abs/2302.14703
Autor:
Taleb, Aiham, Loetzsch, Winfried, Danz, Noel, Severin, Julius, Gaertner, Thomas, Bergner, Benjamin, Lippert, Christoph
Self-supervised learning methods have witnessed a recent surge of interest after proving successful in multiple application fields. In this work, we leverage these techniques, and we propose 3D versions for five different self-supervised methods, in
Externí odkaz:
http://arxiv.org/abs/2006.03829
Akademický článek
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Akademický článek
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Autor:
Meule, Adrian, Kolar, David R., Gärtner, Thomas, Osen, Bernhard, Rauh, Elisabeth, Naab, Silke, Voderholzer, Ulrich
Publikováno v:
In Journal of Psychosomatic Research September 2023 172
