Zobrazeno 1 - 10
of 1 043
pro vyhledávání: '"Zhang Kunpeng"'
Matching patients effectively and efficiently for clinical trials is a significant challenge due to the complexity and variability of patient profiles and trial criteria. This paper presents a novel framework, Multi-Agents for Knowledge Augmentation
Externí odkaz:
http://arxiv.org/abs/2411.14637
This paper studies the distributed bandit convex optimization problem with time-varying inequality constraints, where the goal is to minimize network regret and cumulative constraint violation. To calculate network cumulative constraint violation, ex
Externí odkaz:
http://arxiv.org/abs/2411.11574
In recent years, the application of generative artificial intelligence (GenAI) in financial analysis and investment decision-making has gained significant attention. However, most existing approaches rely on single-agent systems, which fail to fully
Externí odkaz:
http://arxiv.org/abs/2411.04788
Autor:
Sun, Mingwei, Zhang, Kunpeng
Video-based apparent affect detection plays a crucial role in video understanding, as it encompasses various elements such as vision, audio, audio-visual interactions, and spatiotemporal information, which are essential for accurate video predictions
Externí odkaz:
http://arxiv.org/abs/2408.15209
Autor:
Zhang, Kunpeng, Yi, Xinlei, Wen, Guanghui, Cao, Ming, Johansson, Karl H., Chai, Tianyou, Yang, Tao
This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online a
Externí odkaz:
http://arxiv.org/abs/2406.14060
Autor:
Xiao, Changrong, Ma, Wenxing, Song, Qingping, Xu, Sean Xin, Zhang, Kunpeng, Wang, Yufang, Fu, Qi
Receiving timely and personalized feedback is essential for second-language learners, especially when human instructors are unavailable. This study explores the effectiveness of Large Language Models (LLMs), including both proprietary and open-source
Externí odkaz:
http://arxiv.org/abs/2401.06431
This paper focuses on the distributed online convex optimization problem with time-varying inequality constraints over a network of agents, where each agent collaborates with its neighboring agents to minimize the cumulative network-wide loss over ti
Externí odkaz:
http://arxiv.org/abs/2311.01957
Publikováno v:
Network and Distributed System Security (NDSS) Symposium 2024, 26 February - 1 March 2024, San Diego, CA, USA
Mutation-based fuzzing is popular and effective in discovering unseen code and exposing bugs. However, only a few studies have concentrated on quantifying the importance of input bytes, which refers to the degree to which a byte contributes to the di
Externí odkaz:
http://arxiv.org/abs/2308.09239
Image captioning, an important vision-language task, often requires a tremendous number of finely labeled image-caption pairs for learning the underlying alignment between images and texts. In this paper, we proposed a multimodal data augmentation me
Externí odkaz:
http://arxiv.org/abs/2305.01855
Large-scale data missing is a challenging problem in Intelligent Transportation Systems (ITS). Many studies have been carried out to impute large-scale traffic data by considering their spatiotemporal correlations at a network level. In existing traf
Externí odkaz:
http://arxiv.org/abs/2301.11691