Zobrazeno 1 - 10
of 27 108
pro vyhledávání: '"wang, kun"'
Autor:
Yu, Miao, Wang, Shilong, Zhang, Guibin, Mao, Junyuan, Yin, Chenlong, Liu, Qijiong, Wen, Qingsong, Wang, Kun, Wang, Yang
Large language models (LLMs) have empowered nodes within multi-agent networks with intelligence, showing growing applications in both academia and industry. However, how to prevent these networks from generating malicious information remains unexplor
Externí odkaz:
http://arxiv.org/abs/2410.15686
In this paper, we introduce DCDepth, a novel framework for the long-standing monocular depth estimation task. Moving beyond conventional pixel-wise depth estimation in the spatial domain, our approach estimates the frequency coefficients of depth pat
Externí odkaz:
http://arxiv.org/abs/2410.14980
Autor:
Fang, Rongyao, Duan, Chengqi, Wang, Kun, Li, Hao, Tian, Hao, Zeng, Xingyu, Zhao, Rui, Dai, Jifeng, Li, Hongsheng, Liu, Xihui
Recent advancements in multimodal foundation models have yielded significant progress in vision-language understanding. Initial attempts have also explored the potential of multimodal large language models (MLLMs) for visual content generation. Howev
Externí odkaz:
http://arxiv.org/abs/2410.13861
Autor:
Zhang, Guibin, Dong, Haonan, Zhang, Yuchen, Li, Zhixun, Chen, Dingshuo, Wang, Kai, Chen, Tianlong, Liang, Yuxuan, Cheng, Dawei, Wang, Kun
Training high-quality deep models necessitates vast amounts of data, resulting in overwhelming computational and memory demands. Recently, data pruning, distillation, and coreset selection have been developed to streamline data volume by retaining, s
Externí odkaz:
http://arxiv.org/abs/2410.13761
Autor:
Zhou, Zhenhong, Yu, Haiyang, Zhang, Xinghua, Xu, Rongwu, Huang, Fei, Wang, Kun, Liu, Yang, Fang, Junfeng, Li, Yongbin
Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations. In light of this, recent research on safety mechanisms has emerged, reveali
Externí odkaz:
http://arxiv.org/abs/2410.13708
In this study, we investigate the Type-I Two-Higgs-Doublet Model (2HDM-I) as a potential explanation for the 95 GeV diphoton excess reported at the LHC and assess the feasibility of discovering a 95 GeV Higgs boson at future hadron colliders. With th
Externí odkaz:
http://arxiv.org/abs/2410.13636
Autor:
Chen, Nelson, Wang, Kun, Johnson III, William R., Kramer-Bottiglio, Rebecca, Bekris, Kostas, Aanjaneya, Mridul
Tensegrity robots are composed of rigid struts and flexible cables. They constitute an emerging class of hybrid rigid-soft robotic systems and are promising systems for a wide array of applications, ranging from locomotion to assembly. They are diffi
Externí odkaz:
http://arxiv.org/abs/2410.12216
Autor:
Zhang, Guibin, Yue, Yanwei, Sun, Xiangguo, Wan, Guancheng, Yu, Miao, Fang, Junfeng, Wang, Kun, Cheng, Dawei
Recent advancements in large language model (LLM)-based agents have demonstrated that collective intelligence can significantly surpass the capabilities of individual agents, primarily due to well-crafted inter-agent communication topologies. Despite
Externí odkaz:
http://arxiv.org/abs/2410.11782
Autor:
Hanneke, Steve, Wang, Kun
We study the stochastic noisy bandit problem with an unknown reward function $f^*$ in a known function class $\mathcal{F}$. Formally, a model $M$ maps arms $\pi$ to a probability distribution $M(\pi)$ of reward. A model class $\mathcal{M}$ is a colle
Externí odkaz:
http://arxiv.org/abs/2410.09597
Autor:
Jiang, Peng, Wang, Kun, Wang, Jiaxing, Feng, Zeliang, Qiao, Shengjie, Deng, Runhuai, Zhang, Fengkai
GPR full-waveform inversion optimizes the subsurface property model iteratively to match the entire waveform information. However, the model gradients derived from wavefield continuation often contain errors, such as ghost values and excessively larg
Externí odkaz:
http://arxiv.org/abs/2410.08568