Zobrazeno 1 - 10
of 7 180
pro vyhledávání: '"A. Egeland"'
Autor:
A. Egeland
Publikováno v:
History of Geo- and Space Sciences, Vol 15, Pp 27-39 (2024)
From 1901 to 1912 – known as the “heroic period” of Arctic and Antarctic exploration – great inroads were made (not only geographic but also scientific) to our knowledge of the continent. At Amundsen's Expedition through the Northwest Passage
Externí odkaz:
https://doaj.org/article/60c87623db5a4b68a2be571404424bcb
Autor:
Smith, Torbjørn, Egeland, Olav
This paper presents a new method for learning dissipative Hamiltonian dynamics from a limited and noisy dataset. The method uses the Helmholtz decomposition to learn a vector field as the sum of a symplectic and a dissipative vector field. The two ve
Externí odkaz:
http://arxiv.org/abs/2410.18656
Autor:
A. Egeland, W. J. Burke
Publikováno v:
History of Geo- and Space Sciences, Vol 10, Pp 201-213 (2019)
Auroral spectroscopy provided the first tool for remotely sensing the compositions and dynamics of the high-latitude ionosphere. In 1885, Balmer discovered that the visible hydrogen spectrum consists of a series of discrete lines whose wavelengths fo
Externí odkaz:
https://doaj.org/article/3a779462f43a4392be51f486ad277ad5
Diffusion models have seen rapid adoption in robotic imitation learning, enabling autonomous execution of complex dexterous tasks. However, action synthesis is often slow, requiring many steps of iterative denoising, limiting the extent to which mode
Externí odkaz:
http://arxiv.org/abs/2406.04806
Autor:
Mamalakis, Michail, de Vareilles, Héloïse, Wu, Shun-Chin Jim, Agartz, Ingrid, Mørch-Johnsen, Lynn Egeland, Garrison, Jane, Simons, Jon, Lio, Pietro, Suckling, John, Murray, Graham
In the last decade, computer vision has witnessed the establishment of various training and learning approaches. Techniques like adversarial learning, contrastive learning, diffusion denoising learning, and ordinary reconstruction learning have becom
Externí odkaz:
http://arxiv.org/abs/2405.19204
Autor:
Mamalakis, Michail, Mamalakis, Antonios, Agartz, Ingrid, Mørch-Johnsen, Lynn Egeland, Murray, Graham, Suckling, John, Lio, Pietro
The accelerated progress of artificial intelligence (AI) has popularized deep learning models across domains, yet their inherent opacity poses challenges, notably in critical fields like healthcare, medicine and the geosciences. Explainable AI (XAI)
Externí odkaz:
http://arxiv.org/abs/2405.10008
Autor:
Smith, Torbjørn, Egeland, Olav
Publikováno v:
European Journal of Control, 2024
A method for learning Hamiltonian dynamics from a limited and noisy dataset is proposed. The method learns a Hamiltonian vector field on a reproducing kernel Hilbert space (RKHS) of inherently Hamiltonian vector fields, and in particular, odd Hamilto
Externí odkaz:
http://arxiv.org/abs/2404.07703
Autor:
Smith, Torbjørn, Egeland, Olav
Publikováno v:
2024 European Control Conference (ECC)
This paper presents a method for learning Hamiltonian dynamics from a limited set of data points. The Hamiltonian vector field is found by regularized optimization over a reproducing kernel Hilbert space of vector fields that are inherently Hamiltoni
Externí odkaz:
http://arxiv.org/abs/2312.09734
Autor:
A. Egeland, W. J. Burke
Publikováno v:
History of Geo- and Space Sciences, Vol 7, Iss 1, Pp 53-61 (2016)
The Northern Lights Observatory in Tromsø began as Professor Lars Vegard's dream for a permanent facility in northern Norway, dedicated to the continuous study of auroral phenomenology and dynamics. Fortunately, not only was Vegard an internationall
Externí odkaz:
https://doaj.org/article/d06871335a09471e8a6e27620db35e64
Autor:
Mamalakis, Michail, de Vareilles, Heloise, AI-Manea, Atheer, Mitchell, Samantha C., Arartz, Ingrid, Morch-Johnsen, Lynn Egeland, Garrison, Jane, Simons, Jon, Lio, Pietro, Suckling, John, Murray, Graham
The significant features identified in a representative subset of the dataset during the learning process of an artificial intelligence model are referred to as a 'global' explanation. 3D global explanations are crucial in neuroimaging, where a compl
Externí odkaz:
http://arxiv.org/abs/2309.00903