Zobrazeno 1 - 10
of 23 664
pro vyhledávání: '"Schaub, A."'
We present a method, which allows efficient and safe approximation of model predictive controllers using kernel interpolation. Since the computational complexity of the approximating function scales linearly with the number of data points, we propose
Externí odkaz:
http://arxiv.org/abs/2410.06771
In this paper we present a hybrid method for the automatic detection of dermatological pathologies in medical reports. We use a large language model combined with medical ontologies to predict, given a first appointment or follow-up medical report, t
Externí odkaz:
http://arxiv.org/abs/2410.00616
Recent advances in large language models have enabled the creation of highly effective chatbots, which may serve as a platform for targeted advertising. This paper investigates the risks of personalizing advertising in chatbots to their users. We dev
Externí odkaz:
http://arxiv.org/abs/2409.15436
Autor:
Lingenberg, Tobias, Reuter, Markus, Sudhakaran, Gopika, Gojny, Dominik, Roth, Stefan, Schaub-Meyer, Simone
Simple data augmentation techniques, such as rotations and flips, are widely used to enhance the generalization power of computer vision models. However, these techniques often fail to modify high-level semantic attributes of a class. To address this
Externí odkaz:
http://arxiv.org/abs/2408.14584
The dominating set reconfiguration problem is defined as determining, for a given dominating set problem and two among its feasible solutions, whether one is reachable from the other via a sequence of feasible solutions subject to a certain adjacency
Externí odkaz:
http://arxiv.org/abs/2408.07510
Autor:
Hahn, Susana, Martens, Cedric, Nemes, Amade, Otunuya, Henry, Romero, Javier, Schaub, Torsten, Schellhorn, Sebastian
We are interested in automating reasoning with and about study regulations, catering to various stakeholders, ranging from administrators, over faculty, to students at different stages. Our work builds on an extensive analysis of various study progra
Externí odkaz:
http://arxiv.org/abs/2408.04528
Slot attention aims to decompose an input image into a set of meaningful object files (slots). These latent object representations enable various downstream tasks. Yet, these slots often bind to object parts, not objects themselves, especially for re
Externí odkaz:
http://arxiv.org/abs/2407.17929
Attribution maps are one of the most established tools to explain the functioning of computer vision models. They assign importance scores to input features, indicating how relevant each feature is for the prediction of a deep neural network. While m
Externí odkaz:
http://arxiv.org/abs/2407.11910
Autor:
Telyatnikov, Lev, Bernardez, Guillermo, Montagna, Marco, Vasylenko, Pavlo, Zamzmi, Ghada, Hajij, Mustafa, Schaub, Michael T, Miolane, Nina, Scardapane, Simone, Papamarkou, Theodore
This work introduces TopoBenchmarkX, a modular open-source library designed to standardize benchmarking and accelerate research in Topological Deep Learning (TDL). TopoBenchmarkX maps the TDL pipeline into a sequence of independent and modular compon
Externí odkaz:
http://arxiv.org/abs/2406.06642
Residual connections and normalization layers have become standard design choices for graph neural networks (GNNs), and were proposed as solutions to the mitigate the oversmoothing problem in GNNs. However, how exactly these methods help alleviate th
Externí odkaz:
http://arxiv.org/abs/2406.02997