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pro vyhledávání: '"Javadi, Farnoosh"'
Autor:
Javadi, Farnoosh, Gampa, Phanideep, Woo, Alyssa, Geng, Xingxing, Zhang, Hang, Sepulveda, Jose, Bayar, Belhassen, Wang, Fei
Streaming services have reshaped how we discover and engage with digital entertainment. Despite these advancements, effectively understanding the wide spectrum of user search queries continues to pose a significant challenge. An accurate query unders
Externí odkaz:
http://arxiv.org/abs/2409.08931
Autor:
Ataiefard, Foozhan, Ahmed, Walid, Hajimolahoseini, Habib, Asani, Saina, Javadi, Farnoosh, Hassanpour, Mohammad, Awad, Omar Mohamed, Wen, Austin, Liu, Kangling, Liu, Yang
Vision transformers are known to be more computationally and data-intensive than CNN models. These transformer models such as ViT, require all the input image tokens to learn the relationship among them. However, many of these tokens are not informat
Externí odkaz:
http://arxiv.org/abs/2401.15293
Autor:
Javadi, Farnoosh, Ahmed, Walid, Hajimolahoseini, Habib, Ataiefard, Foozhan, Hassanpour, Mohammad, Asani, Saina, Wen, Austin, Awad, Omar Mohamed, Liu, Kangling, Liu, Yang
Massive transformer-based models face several challenges, including slow and computationally intensive pre-training and over-parametrization. This paper addresses these challenges by proposing a versatile method called GQKVA, which generalizes query,
Externí odkaz:
http://arxiv.org/abs/2311.03426
Various data imbalances that naturally arise in a multi-territory personalized recommender system can lead to a significant item bias for globally prevalent items. A locally popular item can be overshadowed by a globally prevalent item. Moreover, use
Externí odkaz:
http://arxiv.org/abs/2310.03148
Akademický článek
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Publikováno v:
ACM International Conference Proceeding Series; 10/2/2017, p3-14, 12p