Zobrazeno 1 - 10
of 22 158
pro vyhledávání: '"A. Anselm"'
We present a randomized algorithm for solving low-degree polynomial equation systems over finite fields faster than exhaustive search. In order to do so, we follow a line of work by Lokshtanov, Paturi, Tamaki, Williams, and Yu (SODA 2017), Bj\"orklun
Externí odkaz:
http://arxiv.org/abs/2410.20162
Visual counterfactual explanation (CF) methods modify image concepts, e.g, shape, to change a prediction to a predefined outcome while closely resembling the original query image. Unlike self-explainable models (SEMs) and heatmap techniques, they gra
Externí odkaz:
http://arxiv.org/abs/2409.12952
Label noise refers to the phenomenon where instances in a data set are assigned to the wrong label. Label noise is harmful to classifier performance, increases model complexity and impairs feature selection. Addressing label noise is crucial, yet cur
Externí odkaz:
http://arxiv.org/abs/2409.08647
Autor:
Petryk, Dmytro, Dyka, Zoya, Kabin, Ievgen, Breitenreiter, Anselm, Schaeffner, Jan, Krstic, Milos
Security requirements for the Internet of things (IoT), wireless sensor nodes, and other wireless devices connected in a network for data exchange are high. These devices are often subject to lab analysis with the objective to reveal secret hidden in
Externí odkaz:
http://arxiv.org/abs/2407.06751
Embedding parameterized optimization problems as layers into machine learning architectures serves as a powerful inductive bias. Training such architectures with stochastic gradient descent requires care, as degenerate derivatives of the embedded opt
Externí odkaz:
http://arxiv.org/abs/2407.05920
Autor:
Fang, Zheng, Arana-Catania, Miguel, van Lier, Felix-Anselm, Velarde, Juliana Outes, Bregazzi, Harry, Airoldi, Mara, Carter, Eleanor, Procter, Rob
The sheer number of research outputs published every year makes systematic reviewing increasingly time- and resource-intensive. This paper explores the use of machine learning techniques to help navigate the systematic review process. ML has previous
Externí odkaz:
http://arxiv.org/abs/2406.16527
Real-world applications of machine learning models are often subject to legal or policy-based regulations. Some of these regulations require ensuring the validity of the model, i.e., the approximation error being smaller than a threshold. A global me
Externí odkaz:
http://arxiv.org/abs/2406.07474
Autor:
Antony, Patrick, Hosters, Norbert, Behr, Marek, Hopf, Anselm, Krämer, Frank, Weber, Carsten, Turner, Paul
Modern diesel engines temporarily use a very late post-injection in the combustion cycle to either generate heat for a diesel particulate filter regeneration or purge a lean NOx trap. In some configurations, unburned fuel is left at the cylinder wall
Externí odkaz:
http://arxiv.org/abs/2406.02702
The average revenue, or market value, of wind and solar energy tends to fall with increasing market shares, as is now evident across European electricity markets. At the same time, these markets have become more interconnected. In this paper, we empi
Externí odkaz:
http://arxiv.org/abs/2405.17166
While recently Large Language Models (LLMs) have achieved remarkable successes, they are vulnerable to certain jailbreaking attacks that lead to generation of inappropriate or harmful content. Manual red-teaming requires finding adversarial prompts t
Externí odkaz:
http://arxiv.org/abs/2404.16873