Zobrazeno 1 - 10
of 157 182
pro vyhledávání: '"Prediction Methods"'
This paper explores the ability of Graph Neural Networks (GNNs) in learning various forms of information for link prediction, alongside a brief review of existing link prediction methods. Our analysis reveals that GNNs cannot effectively learn struct
Externí odkaz:
http://arxiv.org/abs/2411.14711
The rapid growth of online social networks has underscored the importance of understanding the intensity of user relationships, referred to as "tie strength." Over the past few decades, extensive efforts have been made to assess tie strength in netwo
Externí odkaz:
http://arxiv.org/abs/2410.19214
Autor:
Moskalenko, Andrey, Bryncev, Alexey, Vatolin, Dmitry, Timofte, Radu, Zhan, Gen, Yang, Li, Tang, Yunlong, Liao, Yiting, Lin, Jiongzhi, Huang, Baitao, Moradi, Morteza, Moradi, Mohammad, Rundo, Francesco, Spampinato, Concetto, Borji, Ali, Palazzo, Simone, Zhu, Yuxin, Sun, Yinan, Duan, Huiyu, Cao, Yuqin, Jia, Ziheng, Hu, Qiang, Min, Xiongkuo, Zhai, Guangtao, Fang, Hao, Cong, Runmin, Lu, Xiankai, Zhou, Xiaofei, Zhang, Wei, Zhao, Chunyu, Mu, Wentao, Deng, Tao, Tavakoli, Hamed R.
This paper reviews the Challenge on Video Saliency Prediction at AIM 2024. The goal of the participants was to develop a method for predicting accurate saliency maps for the provided set of video sequences. Saliency maps are widely exploited in vario
Externí odkaz:
http://arxiv.org/abs/2409.14827
Autor:
Taylor, Robert
This paper presents the experimental process and results of SVM, Gradient Boosting, and an Attention-GRU Hybrid model in predicting the Implied Volatility of rolled-over five-year spread contracts of credit default swaps (CDS) on European corporate d
Externí odkaz:
http://arxiv.org/abs/2408.15404
Autor:
Lemli, Beáta1,2 (AUTHOR) beata.lemli@pte.hu, Pál, Szilárd1 (AUTHOR) szechenyi.aleksandar@gytk.pte.hu, Salem, Ala'3 (AUTHOR) a.salem@kingston.ac.uk, Széchenyi, Aleksandar1,2 (AUTHOR)
Publikováno v:
International Journal of Molecular Sciences. Nov2024, Vol. 25 Issue 22, p12045. 45p.
Autor:
Pion, Aurélien, Vazquez, Emmanuel
Publikováno v:
LOD 2024, 10th International Conference on Machine Learning, Optimization, and Data Science, Sep 2024, Castiglione della Pescaia Grosseto Italy, Italy
This article advocates the use of conformal prediction (CP) methods for Gaussian process (GP) interpolation to enhance the calibration of prediction intervals. We begin by illustrating that using a GP model with parameters selected by maximum likelih
Externí odkaz:
http://arxiv.org/abs/2407.08271
We develop new conformal inference methods for obtaining validity guarantees on the output of large language models (LLMs). Prior work in conformal language modeling identifies a subset of the text that satisfies a high-probability guarantee of corre
Externí odkaz:
http://arxiv.org/abs/2406.09714
Autor:
Dewolf, Nicolas
In the past decades, most work in the area of data analysis and machine learning was focused on optimizing predictive models and getting better results than what was possible with existing models. To what extent the metrics with which such improvemen
Externí odkaz:
http://arxiv.org/abs/2405.02082
Autor:
Kwon, Hyeji1,2 (AUTHOR), Ko, Soobon1 (AUTHOR), Ha, Kyungsoo3 (AUTHOR), Lee, Jungjoon K.4,5,6 (AUTHOR) jjk.lee@nus.edu.sg, Choi, Yoonjoo1 (AUTHOR) kalicuta@gmail.com
Publikováno v:
PLoS ONE. 8/23/2024, Vol. 19 Issue 8, p1-12. 12p.
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