Zobrazeno 1 - 10
of 22 215
pro vyhledávání: '"Pairwise alignment"'
Graph-based methods, pivotal for label inference over interconnected objects in many real-world applications, often encounter generalization challenges, if the graph used for model training differs significantly from the graph used for testing. This
Externí odkaz:
http://arxiv.org/abs/2403.01092
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Segev, Danny
The main contribution of this paper resides in developing a new algorithmic approach for addressing the continuous-time joint replenishment problem, termed $\Psi$-pairwise alignment. The latter mechanism, through which we synchronize multiple Economi
Externí odkaz:
http://arxiv.org/abs/2302.09941
Autor:
Diab, Safaa, Nassereldine, Amir, Alser, Mohammed, Luna, Juan Gómez, Mutlu, Onur, Hajj, Izzat El
We show that the wavefront algorithm can achieve higher pairwise read alignment throughput on a UPMEM PIM system than on a server-grade multi-threaded CPU system.
Externí odkaz:
http://arxiv.org/abs/2204.02085
PARSE: Pairwise Alignment of Representations in Semi-Supervised EEG Learning for Emotion Recognition
We propose PARSE, a novel semi-supervised architecture for learning strong EEG representations for emotion recognition. To reduce the potential distribution mismatch between the large amounts of unlabeled data and the limited amount of labeled data,
Externí odkaz:
http://arxiv.org/abs/2202.05400
Autor:
Ding, Zhipeng, Niethammer, Marc
Atlas building and image registration are important tasks for medical image analysis. Once one or multiple atlases from an image population have been constructed, commonly (1) images are warped into an atlas space to study intra-subject or inter-subj
Externí odkaz:
http://arxiv.org/abs/2202.03563
Autor:
Cinaglia, Pietro1 (AUTHOR) cinaglia@unicz.it, Cannataro, Mario2 (AUTHOR)
Publikováno v:
Entropy. Apr2023, Vol. 25 Issue 4, p665. 14p.
Autor:
ElNaghy, Hanan1 (AUTHOR) hanan.elnaghy@uva.nl, Dorst, Leo1 (AUTHOR)
Publikováno v:
International Journal of Computer Vision. Sep2022, Vol. 130 Issue 9, p2184-2204. 21p.
Deep neural networks have demonstrated advanced abilities on various visual classification tasks, which heavily rely on the large-scale training samples with annotated ground-truth. However, it is unrealistic always to require such annotation in real
Externí odkaz:
http://arxiv.org/abs/1908.01313