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Bayesian Optimization (BO) is a data-driven strategy for minimizing/maximizing black-box functions based on probabilistic surrogate models. In the presence of safety constraints, the performance of BO crucially relies on tight probabilistic error bou
Externí odkaz:
http://arxiv.org/abs/2411.02253
Despite their success in various vision tasks, deep neural network architectures often underperform in out-of-distribution scenarios due to the difference between training and target domain style. To address this limitation, we introduce One-Shot Sty
Externí odkaz:
http://arxiv.org/abs/2410.00900
In this work, an adaptive predictive control scheme for linear systems with unknown parameters and bounded additive disturbances is proposed. In contrast to related adaptive control approaches that robustly consider the parametric uncertainty, the pr
Externí odkaz:
http://arxiv.org/abs/2409.01955
Rogue waves are associated with various ocean processes, both at the coast and in the open ocean. In either zone, inhomogeneities in the wave field caused by shoaling, crossing seas or current interactions disturb wave statistics, increasing rogue wa
Externí odkaz:
http://arxiv.org/abs/2408.06371
Theoretical studies on the modulation of unidimensional regular waves over a flat bottom due to a current typically assign an asymmetry between the effects of opposing/following streams on the evolution of major sea variables, such as significant wav
Externí odkaz:
http://arxiv.org/abs/2407.04723
Publikováno v:
SPIE Proceedings Volume 13039, Automatic Target Recognition XXXIV; 1303909 (2024)
Visually detecting camouflaged objects is a hard problem for both humans and computer vision algorithms. Strong similarities between object and background appearance make the task significantly more challenging than traditional object detection or se
Externí odkaz:
http://arxiv.org/abs/2406.05776
We present a stochastic constrained output-feedback data-driven predictive control scheme for linear time-invariant systems subject to bounded additive disturbances. The approach uses data-driven predictors based on an extension of Willems' fundament
Externí odkaz:
http://arxiv.org/abs/2402.00681
Publikováno v:
SPIE Proceedings Volume 12534, Infrared Technology and Applications XLIX; 1253417 (2023)
Cameras digitize real-world scenes as pixel intensity values with a limited value range given by the available bits per pixel (bpp). High Dynamic Range (HDR) cameras capture those luminance values in higher resolution through an increase in the numbe
Externí odkaz:
http://arxiv.org/abs/2305.00691
Publikováno v:
In: Andres, B., Bernard, F., Cremers, D., Frintrop, S., Goldl\"ucke, B., Ihrke, I. (eds) Pattern Recognition. DAGM GCPR 2022. Lecture Notes in Computer Science, vol 13485. Springer, Cham
Object detection is one of the key tasks in many applications of computer vision. Deep Neural Networks (DNNs) are undoubtedly a well-suited approach for object detection. However, such DNNs need highly adapted hardware together with hardware-specific
Externí odkaz:
http://arxiv.org/abs/2304.11580
The fundamental lemma from behavioral systems theory yields a data-driven non-parametric system representation that has shown great potential for the data-efficient control of unknown linear and weakly nonlinear systems, even in the presence of measu
Externí odkaz:
http://arxiv.org/abs/2304.03386