Výsledky vyhledávání - "Evaluation Metric"

Akademický článek

Using the Pearson’s correlation coefficient as the sole metric to measure the accuracy of quantitative trait prediction: is it sufficient?

Autor: Shouhui Pan, Zhongqiang Liu, Yanyun Han, Dongfeng Zhang, Xiangyu Zhao, Jinlong Li, Kaiyi Wang

Publikováno v: Frontiers in Plant Science, Vol 15 (2024)

How to evaluate the accuracy of quantitative trait prediction is crucial to choose the best model among several possible choices in plant breeding. Pearson’s correlation coefficient (PCC), serving as a metric for quantifying the strength of the lin

Externí odkaz: https://doaj.org/article/cb98cda2e0114c2b827c66340549854b

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Akademický článek

Initial exploration into sarcasm and irony through machine translation

Autor: Zheng Lin Chia, Michal Ptaszynski, Marzena Karpinska, Juuso Eronen, Fumito Masui

Publikováno v: Natural Language Processing Journal, Vol 9, Iss , Pp 100106- (2024)

In this paper, we investigate sarcasm and irony as seen through a novel perspective of machine translation. We employ various techniques for translation, comparing both manually and automatically translated datasets of irony and sarcasm. We first cla

Externí odkaz: https://doaj.org/article/88aa9f738b49423082a7ca707433b638

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Akademický článek

Perceptual metric for face image quality with pixel-level interpretability

Autor: Jo, Byungho ^a, Park, In Kyu ^{a, b}, Hong, Sungeun ^{c, ⁎}

Publikováno v: In Neurocomputing 21 January 2025 614

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Akademický článek

Initial exploration into sarcasm and irony through machine translation

Autor: Chia, Zheng Lin ^{a, ⁎}, Ptaszynski, Michal ^a, Karpinska, Marzena ^c, Eronen, Juuso ^b, Masui, Fumito ^a

Publikováno v: In Natural Language Processing Journal December 2024 9

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Akademický článek

Low-light image enhancement: A comprehensive review on methods, datasets and evaluation metrics

Autor: Jingchun, Zhan ^a, Eg Su, Goh ^{a, ⁎}, Shahrizal Sunar, Mohd ^{a, b}

Publikováno v: In Journal of King Saud University - Computer and Information Sciences December 2024 36(10)

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Akademický článek

Framework for evaluating code generation ability of large language models

Autor: Sangyeop Yeo, Yu-Seung Ma, Sang Cheol Kim, Hyungkook Jun, Taeho Kim

Publikováno v: ETRI Journal, Vol 46, Iss 1, Pp 106-117 (2024)

Large language models (LLMs) have revolutionized various applications in natural language processing and exhibited proficiency in generating programming code. We propose a framework for evaluating the code generation ability of LLMs and introduce a n

Externí odkaz: https://doaj.org/article/0149d44d4b2944788e506b1366390abf

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