Zobrazeno 1 - 10
of 894
pro vyhledávání: '"Zhu, Liming"'
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
The robustness of large language models (LLMs) becomes increasingly important as their use rapidly grows in a wide range of domains. Retrieval-Augmented Generation (RAG) is considered as a means to improve the trustworthiness of text generation from
Externí odkaz:
http://arxiv.org/abs/2402.07179
Autor:
Zhang, Dawen, Xia, Boming, Liu, Yue, Xu, Xiwei, Hoang, Thong, Xing, Zhenchang, Staples, Mark, Lu, Qinghua, Zhu, Liming
The advent of Generative AI has marked a significant milestone in artificial intelligence, demonstrating remarkable capabilities in generating realistic images, texts, and data patterns. However, these advancements come with heightened concerns over
Externí odkaz:
http://arxiv.org/abs/2311.18252
Artificial Intelligence (AI), particularly through the advent of large-scale generative AI (GenAI) models such as Large Language Models (LLMs), has become a transformative element in contemporary technology. While these models have unlocked new possi
Externí odkaz:
http://arxiv.org/abs/2311.13158
Foundation models, such as large language models (LLMs), have been widely recognised as transformative AI technologies due to their capabilities to understand and generate content, including plans with reasoning capabilities. Foundation model based a
Externí odkaz:
http://arxiv.org/abs/2311.13148