Zobrazeno 1 - 10
of 4 122
pro vyhledávání: '"hybrid loss"'
Autor:
Aslam, Muhammad Azeem, Jun, Wang, Ahmed, Nisar, Zaman, Muhammad Imran, Yanan, Li, Hongfei, Hu, Shiyu, Wang, Liu, Xin
In multi-label emotion classification, particularly for low-resource languages like Arabic, the challenges of class imbalance and label correlation hinder model performance, especially in accurately predicting minority emotions. To address these issu
Externí odkaz:
http://arxiv.org/abs/2410.03979
Accurate time series forecasting, predicting future values based on past data, is crucial for diverse industries. Many current time series methods decompose time series into multiple sub-series, applying different model architectures and training wit
Externí odkaz:
http://arxiv.org/abs/2411.11340
Since 2019, the global COVID-19 outbreak has emerged as a crucial focus in healthcare research. Although RT-PCR stands as the primary method for COVID-19 detection, its extended detection time poses a significant challenge. Consequently, supplementin
Externí odkaz:
http://arxiv.org/abs/2403.10880
Autor:
Ji, Yi1 (AUTHOR), Luo, Yuxuan1 (AUTHOR) 2222107005@stmail.ujs.edu.cn, Lu, Aixia1 (AUTHOR), Xia, Duanyang1 (AUTHOR), Yang, Lixia2 (AUTHOR), Wee-Chung Liew, Alan3 (AUTHOR)
Publikováno v:
Scientific Reports. 10/10/2024, Vol. 14 Issue 1, p1-18. 18p.
In this report, we introduce Piccolo2, an embedding model that surpasses other models in the comprehensive evaluation over 6 tasks on CMTEB benchmark, setting a new state-of-the-art. Piccolo2 primarily leverages an efficient multi-task hybrid loss tr
Externí odkaz:
http://arxiv.org/abs/2405.06932
Pansharpening aims to enhance remote sensing image (RSI) quality by merging high-resolution panchromatic (PAN) with multispectral (MS) images. However, prior techniques struggled to optimally fuse PAN and MS images for enhanced spatial and spectral i
Externí odkaz:
http://arxiv.org/abs/2404.01121
Publikováno v:
Neural Computing & Applications. Jun2024, Vol. 36 Issue 17, p9753-9766. 14p.
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-18 (2024)
Abstract The prediction of stock market fluctuations is crucial for decision-making in various financial fields. Deep learning algorithms have demonstrated outstanding performance in stock market index prediction. Recent research has also highlighted
Externí odkaz:
https://doaj.org/article/44ebb20221a34099a5cb9697d3b7b10f
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