Zobrazeno 1 - 10
of 2 856
pro vyhledávání: '"Wang, ZiHan"'
Autor:
Xu, Hongshen, Zhu, Su, Wang, Zihan, Zheng, Hang, Ma, Da, Cao, Ruisheng, Fan, Shuai, Chen, Lu, Yu, Kai
Large Language Models (LLMs) have extended their capabilities beyond language generation to interact with external systems through tool calling, offering powerful potential for real-world applications. However, the phenomenon of tool hallucinations,
Externí odkaz:
http://arxiv.org/abs/2412.04141
Autor:
Wang, Zihan, Lee, Gim Hee
We introduce Generalizable 3D-Language Feature Fields (g3D-LF), a 3D representation model pre-trained on large-scale 3D-language dataset for embodied tasks. Our g3D-LF processes posed RGB-D images from agents to encode feature fields for: 1) Novel vi
Externí odkaz:
http://arxiv.org/abs/2411.17030
Autor:
Wang, Zihan, Liang, Brian, Dhat, Varad, Brumbaugh, Zander, Walker, Nick, Krishna, Ranjay, Cakmak, Maya
Understanding robot behaviors and experiences through natural language is crucial for developing intelligent and transparent robotic systems. Recent advancement in large language models (LLMs) makes it possible to translate complex, multi-modal robot
Externí odkaz:
http://arxiv.org/abs/2411.12960
As video generation models advance rapidly, assessing the quality of generated videos has become increasingly critical. Existing metrics, such as Fr\'echet Video Distance (FVD), Inception Score (IS), and ClipSim, measure quality primarily in latent s
Externí odkaz:
http://arxiv.org/abs/2411.13609
Retrieval-augmented generation (RAG) has gained wide attention as the key component to improve generative models with external knowledge augmentation from information retrieval. It has shown great prominence in enhancing the functionality and perform
Externí odkaz:
http://arxiv.org/abs/2410.20598
With the development of intelligent connected vehicle technology, human-machine shared control has gained popularity in vehicle following due to its effectiveness in driver assistance. However, traditional vehicle following systems struggle to mainta
Externí odkaz:
http://arxiv.org/abs/2410.18007
Autor:
Zhang, Ronghui, Yang, Shangyu, Lyu, Dakang, Wang, Zihan, Chen, Junzhou, Ren, Yilong, Gao, Bolin, Lv, Zhihan
Road ponding, a prevalent traffic hazard, poses a serious threat to road safety by causing vehicles to lose control and leading to accidents ranging from minor fender benders to severe collisions. Existing technologies struggle to accurately identify
Externí odkaz:
http://arxiv.org/abs/2410.16999
Autor:
Fu, Xiaohan, Li, Shuheng, Wang, Zihan, Liu, Yihao, Gupta, Rajesh K., Berg-Kirkpatrick, Taylor, Fernandes, Earlence
Large Language Model (LLM) Agents are an emerging computing paradigm that blends generative machine learning with tools such as code interpreters, web browsing, email, and more generally, external resources. These agent-based systems represent an eme
Externí odkaz:
http://arxiv.org/abs/2410.14923
Autor:
Wang, Zihan, Yang, Daniel W., Liu, Zerui, Yan, Evan, Sun, Heming, Ge, Ning, Hu, Miao, Wu, Wei
This study presents the first implementation of multilayer neural networks on a memristor/CMOS integrated system on chip (SoC) to simultaneously detect multiple diseases. To overcome limitations in medical data, generative AI techniques are used to e
Externí odkaz:
http://arxiv.org/abs/2410.14882
Autor:
Lyu, Yougang, Yan, Lingyong, Wang, Zihan, Yin, Dawei, Ren, Pengjie, de Rijke, Maarten, Ren, Zhaochun
As large language models (LLMs) are rapidly advancing and achieving near-human capabilities, aligning them with human values is becoming more urgent. In scenarios where LLMs outperform humans, we face a weak-to-strong alignment problem where we need
Externí odkaz:
http://arxiv.org/abs/2410.07672