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pro vyhledávání: '"Akesson P"'
We address multi-view pedestrian detection in a setting where labeled data is collected using a multi-camera setup different from the one used for testing. While recent multi-view pedestrian detectors perform well on the camera rig used for training,
Externí odkaz:
http://arxiv.org/abs/2412.04117
Reinforcement learning (RL) shows promise in control problems, but its practical application is often hindered by the complexity arising from intricate reward functions with constraints. While the reward hypothesis suggests these competing demands ca
Externí odkaz:
http://arxiv.org/abs/2410.16790
Integrated Sensing and Communication (ISAC) systems are prone to privacy violations, once they aim at handling sensitive identifiable information in several applications. This paper raises the necessity of implementing privacy-preservation measures o
Externí odkaz:
http://arxiv.org/abs/2409.12874
Autor:
Akesson, Simon, Santos, Frances A.
Providing external knowledge to Large Language Models (LLMs) is a key point for using these models in real-world applications for several reasons, such as incorporating up-to-date content in a real-time manner, providing access to domain-specific kno
Externí odkaz:
http://arxiv.org/abs/2406.00029
Akademický článek
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We consider unsupervised domain adaptation (UDA) for semantic segmentation in which the model is trained on a labeled source dataset and adapted to an unlabeled target dataset. Unfortunately, current self-training methods are susceptible to misclassi
Externí odkaz:
http://arxiv.org/abs/2403.03854
Autor:
Jaubert, Olivier, Pascale, Michele, Montalt-Tordera, Javier, Akesson, Julius, Virsinskaite, Ruta, Knight, Daniel, Arridge, Simon, Steeden, Jennifer, Muthurangu, Vivek
Purpose: To develop and assess a deep learning (DL) pipeline to learn dynamic MR image reconstruction from publicly available natural videos (Inter4K). Materials and Methods: Learning was performed for a range of DL architectures (VarNet, 3D UNet, Fa
Externí odkaz:
http://arxiv.org/abs/2311.13963
Autor:
Åkesson, Torsten, Bravo, Cameron, Brennan, Liam, Bryngemark, Lene Kristian, Butti, Pierfrancesco, Dukes, E. Craig, Dutta, Valentina, Echenard, Bertrand, Eichlersmith, Thomas, Eisch, Jonathan, Elén, Einar, Ehrlich, Ralf, Froemming, Cooper, Furmanski, Andrew, Gogate, Niramay, Grieco, Chiara, Group, Craig, Herde, Hannah, Herwig, Christian, Hitlin, David G., Horoho, Tyler, Incandela, Joseph, Ketchum, Wesley, Krnjaic, Gordan, Li, Amina, Mans, Jeremiah, Masterson, Phillip, Middleton, Sophie, Moreno, Omar, Mullier, Geoffrey, Muse, Joseph, Nelson, Timothy, O'Dwyer, Rory, Östman, Leo, Oyang, James, Pascadlo, Jessica, Pöttgen, Ruth, Sarmiento, Luis G., Schuster, Philip, Solt, Matthew, Suarez, Cristina Mantilla, Tompkins, Lauren, Toro, Natalia, Tran, Nhan, Wallin, Erik, Whitbeck, Andrew, Zhang, Danyi
The Light Dark Matter eXperiment (LDMX) is an electron-beam fixed-target experiment designed to achieve comprehensive model independent sensitivity to dark matter particles in the sub-GeV mass region. An upgrade to the LCLS-II accelerator will increa
Externí odkaz:
http://arxiv.org/abs/2308.15173
Akademický článek
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Autor:
Olivier Jaubert, Michele Pascale, Javier Montalt-Tordera, Julius Akesson, Ruta Virsinskaite, Daniel Knight, Simon Arridge, Jennifer Steeden, Vivek Muthurangu
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-10 (2024)
Abstract To develop and assess a deep learning (DL) pipeline to learn dynamic MR image reconstruction from publicly available natural videos (Inter4K). Learning was performed for a range of DL architectures (VarNet, 3D UNet, FastDVDNet) and correspon
Externí odkaz:
https://doaj.org/article/5a7a8d4bd2594e2790bb7d058cc85d40
