Zobrazeno 1 - 10
of 9 131
pro vyhledávání: '"multi-view learning"'
Multi-view learning methods leverage multiple data sources to enhance perception by mining correlations across views, typically relying on predefined categories. However, deploying these models in real-world scenarios presents two primary openness ch
Externí odkaz:
http://arxiv.org/abs/2412.12596
Self-supervised learning (SSL) offers a powerful way to learn robust, generalizable representations without labeled data. In music, where labeled data is scarce, existing SSL methods typically use generated supervision and multi-view redundancy to cr
Externí odkaz:
http://arxiv.org/abs/2411.02711
Multi-view learning methods often focus on improving decision accuracy, while neglecting the decision uncertainty, limiting their suitability for safety-critical applications. To mitigate this, researchers propose trusted multi-view learning methods
Externí odkaz:
http://arxiv.org/abs/2410.03796
The classification performance of the random vector functional link (RVFL), a randomized neural network, has been widely acknowledged. However, due to its shallow learning nature, RVFL often fails to consider all the relevant information available in
Externí odkaz:
http://arxiv.org/abs/2409.04743
Software development is a repetitive task, as developers usually reuse or get inspiration from existing implementations. Code search, which refers to the retrieval of relevant code snippets from a codebase according to the developer's intent that has
Externí odkaz:
http://arxiv.org/abs/2408.09345
Autor:
Nguyen, Huy-Son, Bui, Tuan-Nghia, Nguyen, Long-Hai, Manh-Hung, Hoang, Nguyen, Cam-Van Thi, Le, Hoang-Quynh, Le, Duc-Trong
Bundle recommendation aims to enhance business profitability and user convenience by suggesting a set of interconnected items. In real-world scenarios, leveraging the impact of asymmetric item affiliations is crucial for effective bundle modeling and
Externí odkaz:
http://arxiv.org/abs/2408.08906
Autor:
Wang, Xin-Fei1 (AUTHOR), Huang, Lan1 (AUTHOR) huanglan@jlu.edu.cn, Wang, Yan1 (AUTHOR) huanglan@jlu.edu.cn, Guan, Ren-Chu2 (AUTHOR), You, Zhu-Hong3 (AUTHOR), Sheng, Nan1 (AUTHOR), Xie, Xu-Ping1 (AUTHOR), Hou, Wen-Ju1 (AUTHOR)
Publikováno v:
Briefings in Bioinformatics. Nov2024, Vol. 25 Issue 6, p1-15. 15p.
The fundamental problem with ultrasound-guided diagnosis is that the acquired images are often 2-D cross-sections of a 3-D anatomy, potentially missing important anatomical details. This limitation leads to challenges in ultrasound echocardiography,
Externí odkaz:
http://arxiv.org/abs/2409.09680
Uncertainty quantification for multi-view learning is motivated by the increasing use of multi-view data in scientific problems. A common variant of multi-view learning is late fusion: train separate predictors on individual views and combine them af
Externí odkaz:
http://arxiv.org/abs/2405.16246
Publikováno v:
In Information Sciences March 2025 694