Zobrazeno 1 - 10
of 18 567
pro vyhledávání: '"Heimann, A"'
Egocentric vision aims to capture and analyse the world from the first-person perspective. We explore the possibilities for egocentric wearable devices to improve and enhance industrial use cases w.r.t. data collection, annotation, labelling and down
Externí odkaz:
http://arxiv.org/abs/2406.07738
Autor:
Ha, Cuong Nhat, Asaadi, Shima, Karn, Sanjeev Kumar, Farri, Oladimeji, Heimann, Tobias, Runkler, Thomas
Vision-language models, while effective in general domains and showing strong performance in diverse multi-modal applications like visual question-answering (VQA), struggle to maintain the same level of effectiveness in more specialized domains, e.g.
Externí odkaz:
http://arxiv.org/abs/2404.16192
We present an iterative optimal control method of quantum systems, aimed at an implementation of a desired operation with optimal fidelity. The update step of the method is based on the linear response of the fidelity to the control operators, and it
Externí odkaz:
http://arxiv.org/abs/2404.10462
Autor:
Galtier, Eric, Khaghani, Dimitri, Boiadjieva, Nina, Bhogadi, Dileep, Dyer, Gilliss, Heimann, Phil, Gleason, Arianna E., Pandolfi, Silvia, Makita, Mikako, Seiboth, Frank, Lee, Hae Ja, Nagler, Bob
The last decade has shown the great potential that X-ray Free Electron Lasers have to study High Energy Density matter. Experiments at FELs have made significant breakthroughs in Shock Physics and Dynamic Diffraction, Dense Plasma Physics and Warm De
Externí odkaz:
http://arxiv.org/abs/2405.01551
The $k$-Opt algorithm is a local search algorithm for the Traveling Salesman Problem. Starting with an initial tour, it iteratively replaces at most $k$ edges in the tour with the same number of edges to obtain a better tour. Krentel (FOCS 1989) show
Externí odkaz:
http://arxiv.org/abs/2402.07061
While graph neural networks (GNNs) are widely used for node and graph representation learning tasks, the reliability of GNN uncertainty estimates under distribution shifts remains relatively under-explored. Indeed, while post-hoc calibration strategi
Externí odkaz:
http://arxiv.org/abs/2401.03350
Autor:
Heimann, Fabian, Lehrenfeld, Christoph
In [Heimann, Lehrenfeld, Preu{\ss}, SIAM J. Sci. Comp. 45(2), 2023, B139 - B165] new geometrically unfitted space-time Finite Element methods for partial differential equations posed on moving domains of higher-order accuracy in space and time have b
Externí odkaz:
http://arxiv.org/abs/2311.02348
Publikováno v:
Review of Accounting and Finance, 2024, Vol. 23, Issue 4, pp. 506-536.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/RAF-07-2023-0212
Autor:
Lobre-Lebraty, Katia, Heimann, Marco
Publikováno v:
Management Decision, 2024, Vol. 62, Issue 7, pp. 2214-2232.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/MD-06-2023-0979
Safe deployment of graph neural networks (GNNs) under distribution shift requires models to provide accurate confidence indicators (CI). However, while it is well-known in computer vision that CI quality diminishes under distribution shift, this beha
Externí odkaz:
http://arxiv.org/abs/2309.10976