Zobrazeno 1 - 10
of 318
pro vyhledávání: '"Wang, Yu‐Kai"'
Autor:
Sun, Pei-Fa, Song, Yujae, Gao, Kang-Yu, Wang, Yu-Kai, Zhou, Changjun, Jeon, Sang-Woon, Zhang, Jun
UAVs are increasingly becoming vital tools in various wireless communication applications including internet of things (IoT) and sensor networks, thanks to their rapid and agile non-terrestrial mobility. Despite recent research, planning three-dimens
Externí odkaz:
http://arxiv.org/abs/2410.05759
The remarkable success of large language models (LLMs) across various multi-modality applications is well established. However, integrating large language models with humans, or brain dynamics, remains relatively unexplored. In this paper, we introdu
Externí odkaz:
http://arxiv.org/abs/2409.00121
Autor:
Chang, Kai-Wei, Wu, Haibin, Wang, Yu-Kai, Wu, Yuan-Kuei, Shen, Hua, Tseng, Wei-Cheng, Kang, Iu-thing, Li, Shang-Wen, Lee, Hung-yi
Publikováno v:
in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 32, pp. 3730-3744, 2024
Prompting has become a practical method for utilizing pre-trained language models (LMs). This approach offers several advantages. It allows an LM to adapt to new tasks with minimal training and parameter updates, thus achieving efficiency in both sto
Externí odkaz:
http://arxiv.org/abs/2408.13040
This paper presents a pioneering exploration into the integration of fine-grained human supervision within the autonomous driving domain to enhance system performance. The current advances in End-to-End autonomous driving normally are data-driven and
Externí odkaz:
http://arxiv.org/abs/2408.10908
Driving under drowsy conditions significantly escalates the risk of vehicular accidents. Although recent efforts have focused on using electroencephalography to detect drowsiness, helping prevent accidents caused by driving in such states, seamless h
Externí odkaz:
http://arxiv.org/abs/2408.07083
Autor:
Zhou, Jinzhao, Duan, Yiqun, Zhao, Ziyi, Chang, Yu-Cheng, Wang, Yu-Kai, Do, Thomas, Lin, Chin-Teng
Decoding linguistic information from non-invasive brain signals using EEG has gained increasing research attention due to its vast applicational potential. Recently, a number of works have adopted a generative-based framework to decode electroencepha
Externí odkaz:
http://arxiv.org/abs/2408.04679
Current multi-modality driving frameworks normally fuse representation by utilizing attention between single-modality branches. However, the existing networks still suppress the driving performance as the Image and LiDAR branches are independent and
Externí odkaz:
http://arxiv.org/abs/2405.07573
Multi-agent systems often require agents to collaborate with or compete against other agents with diverse goals, behaviors, or strategies. Agent modeling is essential when designing adaptive policies for intelligent machine agents in multiagent syste
Externí odkaz:
http://arxiv.org/abs/2401.00132
The translation of brain dynamics into natural language is pivotal for brain-computer interfaces (BCIs). With the swift advancement of large language models, such as ChatGPT, the need to bridge the gap between the brain and languages becomes increasi
Externí odkaz:
http://arxiv.org/abs/2309.14030
This paper presents BELT, a novel model and learning framework for the pivotal topic of brain-to-language translation research. The translation from noninvasive brain signals into readable natural language has the potential to promote the application
Externí odkaz:
http://arxiv.org/abs/2309.12056