Zobrazeno 1 - 10
of 29
pro vyhledávání: '"YE Guangnan"'
Publikováno v:
网络与信息安全学报, Vol 10, Pp 156-174 (2024)
Data, recognized as a fundamental strategic resource and key production factor for a nation, has served as the foundational resource and innovation engine for economic and social development. The financial industry, characterized by its data-intensiv
Externí odkaz:
https://doaj.org/article/434b817464dd4e09abc1495d65953c1e
Publikováno v:
Security and Safety, Vol 3, p 2024005 (2024)
Federated Learning (FL) heralds a paradigm shift in the training of artificial intelligence (AI) models by fostering collaborative model training while safeguarding client data privacy. In sectors where data sensitivity and AI model security are of p
Externí odkaz:
https://doaj.org/article/a25854ff8bce45c794f4c5222aadf590
As deep learning models are increasingly deployed in safety-critical applications, evaluating their vulnerabilities to adversarial perturbations is essential for ensuring their reliability and trustworthiness. Over the past decade, a large number of
Externí odkaz:
http://arxiv.org/abs/2411.15210
Recent advancements in Large Vision-Language Models (VLMs) have underscored their superiority in various multimodal tasks. However, the adversarial robustness of VLMs has not been fully explored. Existing methods mainly assess robustness through unim
Externí odkaz:
http://arxiv.org/abs/2405.17894
Autor:
Zhou, Liuzhi, He, Yu, Zhai, Kun, Liu, Xiang, Liu, Sen, Ma, Xingjun, Ye, Guangnan, Jiang, Yu-Gang, Chai, Hongfeng
Federated learning (FL) has emerged as a prominent approach for collaborative training of machine learning models across distributed clients while preserving data privacy. However, the quest to balance acceleration and stability becomes a significant
Externí odkaz:
http://arxiv.org/abs/2405.11811
Federated learning (FL) is a collaborative learning paradigm that allows different clients to train one powerful global model without sharing their private data. Although FL has demonstrated promising results in various applications, it is known to s
Externí odkaz:
http://arxiv.org/abs/2404.11888
Large language models (LLMs) are increasingly being applied across various specialized fields, leveraging their extensive knowledge to empower a multitude of scenarios within these domains. However, each field encompasses a variety of specific tasks
Externí odkaz:
http://arxiv.org/abs/2404.04949
Fraud detection remains a challenging task due to the complex and deceptive nature of fraudulent activities. Current approaches primarily concentrate on learning only one perspective of the graph: either the topological structure of the graph or the
Externí odkaz:
http://arxiv.org/abs/2402.17472
Autor:
Zhou, Yuhang, Ni, Yuchen, Gan, Yunhui, Yin, Zhangyue, Liu, Xiang, Zhang, Jian, Liu, Sen, Qiu, Xipeng, Ye, Guangnan, Chai, Hongfeng
Large Language Models (LLMs) are increasingly adopted in financial analysis for interpreting complex market data and trends. However, their use is challenged by intrinsic biases (e.g., risk-preference bias) and a superficial understanding of market i
Externí odkaz:
http://arxiv.org/abs/2402.12713
Autor:
Zhou, Yuhang, He, Yu, Tian, Siyu, Ni, Yuchen, Yin, Zhangyue, Liu, Xiang, Ji, Chuanjun, Liu, Sen, Qiu, Xipeng, Ye, Guangnan, Chai, Hongfeng
While current tasks of converting natural language to SQL (NL2SQL) using Foundation Models have shown impressive achievements, adapting these approaches for converting natural language to Graph Query Language (NL2GQL) encounters hurdles due to the di
Externí odkaz:
http://arxiv.org/abs/2311.01862