Zobrazeno 1 - 10
of 919
pro vyhledávání: '"Zhu LiMing"'
Autor:
Zhou, Jingwen, Lu, Qinghua, Chen, Jieshan, Zhu, Liming, Xu, Xiwei, Xing, Zhenchang, Harrer, Stefan
The rapid advancement of AI technology has led to widespread applications of agent systems across various domains. However, the need for detailed architecture design poses significant challenges in designing and operating these systems. This paper in
Externí odkaz:
http://arxiv.org/abs/2408.02920
The rapid advancement and widespread deployment of foundation model (FM) based systems have revolutionized numerous applications across various domains. However, the fast-growing capabilities and autonomy have also raised significant concerns about r
Externí odkaz:
http://arxiv.org/abs/2408.02205
The rapid growth of Artificial Intelligence (AI) has underscored the urgent need for responsible AI practices. Despite increasing interest, a comprehensive AI risk assessment toolkit remains lacking. This study introduces our Responsible AI (RAI) Que
Externí odkaz:
http://arxiv.org/abs/2408.11820
Autor:
Lee, Sung Une, Perera, Harsha, Liu, Yue, Xia, Boming, Lu, Qinghua, Zhu, Liming, Cairns, Jessica, Nottage, Moana
Artificial Intelligence (AI) is a widely developed and adopted technology across entire industry sectors. Integrating environmental, social, and governance (ESG) considerations with AI investments is crucial for ensuring ethical and sustainable techn
Externí odkaz:
http://arxiv.org/abs/2408.00965
Workloads in data processing clusters are often represented in the form of DAG (Directed Acyclic Graph) jobs. Scheduling DAG jobs is challenging. Simple heuristic scheduling algorithms are often adopted in practice in production data centres. There i
Externí odkaz:
http://arxiv.org/abs/2405.19131
Autor:
Zhou, Guanglin, Han, Zhongyi, Chen, Shiming, Huang, Biwei, Zhu, Liming, Khan, Salman, Gao, Xin, Yao, Lina
Recent studies indicate that large multimodal models (LMMs) are highly robust against natural distribution shifts, often surpassing previous baselines. Despite this, domain-specific adaptation is still necessary, particularly in specialized areas lik
Externí odkaz:
http://arxiv.org/abs/2405.12217
Autor:
Liu, Yue, Lo, Sin Kit, Lu, Qinghua, Zhu, Liming, Zhao, Dehai, Xu, Xiwei, Harrer, Stefan, Whittle, Jon
Foundation model-enabled generative artificial intelligence facilitates the development and implementation of agents, which can leverage distinguished reasoning and language processing capabilities to takes a proactive, autonomous role to pursue user
Externí odkaz:
http://arxiv.org/abs/2405.10467
The exploitation of publicly accessible data has led to escalating concerns regarding data privacy and intellectual property (IP) breaches in the age of artificial intelligence. As a strategy to safeguard both data privacy and IP-related domain knowl
Externí odkaz:
http://arxiv.org/abs/2405.03316
The advent of advanced AI underscores the urgent need for comprehensive safety evaluations, necessitating collaboration across communities (i.e., AI, software engineering, and governance). However, divergent practices and terminologies across these c
Externí odkaz:
http://arxiv.org/abs/2404.05388
Recent studies have shown that recommender systems (RSs) are highly vulnerable to data poisoning attacks. Understanding attack tactics helps improve the robustness of RSs. We intend to develop efficient attack methods that use limited resources to ge
Externí odkaz:
http://arxiv.org/abs/2402.09023