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pro vyhledávání: '"Guo Dongliang"'
Large pre-trained models have achieved notable success across a range of downstream tasks. However, recent research shows that a type of adversarial attack ($\textit{i.e.,}$ backdoor attack) can manipulate the behavior of machine learning models thro
Externí odkaz:
http://arxiv.org/abs/2410.18267
Retrieval-Augmented Generation (RAG) is widely adopted for its effectiveness and cost-efficiency in mitigating hallucinations and enhancing the domain-specific generation capabilities of large language models (LLMs). However, is this effectiveness an
Externí odkaz:
http://arxiv.org/abs/2410.07589
Graph neural networks (GNNs) have emerged as a powerful tool for analyzing and learning from complex data structured as graphs, demonstrating remarkable effectiveness in various applications, such as social network analysis, recommendation systems, a
Externí odkaz:
http://arxiv.org/abs/2404.17511
Autor:
Zhou, Zhongliang, Hu, Mengxuan, Salcedo, Mariah, Gravel, Nathan, Yeung, Wayland, Venkat, Aarya, Guo, Dongliang, Zhang, Jielu, Kannan, Natarajan, Li, Sheng
Artificial intelligence (AI), particularly machine learning and deep learning models, has significantly impacted bioinformatics research by offering powerful tools for analyzing complex biological data. However, the lack of interpretability and trans
Externí odkaz:
http://arxiv.org/abs/2312.06082
As AI systems have obtained significant performance to be deployed widely in our daily live and human society, people both enjoy the benefits brought by these technologies and suffer many social issues induced by these systems. To make AI systems goo
Externí odkaz:
http://arxiv.org/abs/2308.12315
Tackling unfairness in graph learning models is a challenging task, as the unfairness issues on graphs involve both attributes and topological structures. Existing work on fair graph learning simply assumes that attributes of all nodes are available
Externí odkaz:
http://arxiv.org/abs/2302.12977
Anomaly detection and localization of visual data, including images and videos, are of great significance in both machine learning academia and applied real-world scenarios. Despite the rapid development of visual anomaly detection techniques in rece
Externí odkaz:
http://arxiv.org/abs/2302.06670
Publikováno v:
In Alexandria Engineering Journal February 2025 114:112-122
Publikováno v:
In Thermal Science and Engineering Progress September 2024 54
Akademický článek
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