Zobrazeno 1 - 10
of 10 681 940
pro vyhledávání: '"Zhang, An"'
This paper proposes a novel fast online methodology for outlier detection called the exception maximization outlier detection method(EMODM), which employs probabilistic models and statistical algorithms to detect abnormal patterns from the outputs of
Externí odkaz:
http://arxiv.org/abs/2407.04248
Autor:
Bai, Ye, Chen, Jingping, Chen, Jitong, Chen, Wei, Chen, Zhuo, Ding, Chen, Dong, Linhao, Dong, Qianqian, Du, Yujiao, Gao, Kepan, Gao, Lu, Guo, Yi, Han, Minglun, Han, Ting, Hu, Wenchao, Hu, Xinying, Hu, Yuxiang, Hua, Deyu, Huang, Lu, Huang, Mingkun, Huang, Youjia, Jin, Jishuo, Kong, Fanliu, Lan, Zongwei, Li, Tianyu, Li, Xiaoyang, Li, Zeyang, Lin, Zehua, Liu, Rui, Liu, Shouda, Lu, Lu, Lu, Yizhou, Ma, Jingting, Ma, Shengtao, Pei, Yulin, Shen, Chen, Tan, Tian, Tian, Xiaogang, Tu, Ming, Wang, Bo, Wang, Hao, Wang, Yuping, Wang, Yuxuan, Xia, Hanzhang, Xia, Rui, Xie, Shuangyi, Xu, Hongmin, Yang, Meng, Zhang, Bihong, Zhang, Jun, Zhang, Wanyi, Zhang, Yang, Zhang, Yawei, Zheng, Yijie, Zou, Ming
Modern automatic speech recognition (ASR) model is required to accurately transcribe diverse speech signals (from different domains, languages, accents, etc) given the specific contextual information in various application scenarios. Classic end-to-e
Externí odkaz:
http://arxiv.org/abs/2407.04675
Spin waves, or magnons, are fundamental excitations in magnetic materials that provide insights into their dynamic properties and interactions. Magnons are the building blocks of magnonics, which offer promising perspectives for data storage, quantum
Externí odkaz:
http://arxiv.org/abs/2407.04457
Autor:
Li, Shu-Ang, Meng, Xiao-Yan, Zhang, Su, Zhang, Ying-Jie, Yang, Run-Zhou, Wang, Dian-Dian, Yang, Yang, Liu, Pei-Pei, Kang, Jian-Sheng
An accurate map of intracellular organelle pH is crucial for comprehending cellular metabolism and organellar functions. However, a unified intracellular pH spectrum using a single probe is still lack. Here, we developed a novel quantum entanglement-
Externí odkaz:
http://arxiv.org/abs/2407.04232
Numerous locomotion controllers have been designed based on Reinforcement Learning (RL) to facilitate blind quadrupedal locomotion traversing challenging terrains. Nevertheless, locomotion control is still a challenging task for quadruped robots trav
Externí odkaz:
http://arxiv.org/abs/2407.04224
Autor:
Yang, Hao, Lu, Hongyuan, Zeng, Xinhua, Liu, Yang, Zhang, Xiang, Yang, Haoran, Zhang, Yumeng, Wei, Yiran, Lam, Wai
In the rapidly evolving field of natural language processing, dialogue systems primarily employ a single-step dialogue paradigm. Although this paradigm is efficient, it lacks the depth and fluidity of human interactions and does not appear natural. W
Externí odkaz:
http://arxiv.org/abs/2407.04093
Low sample efficiency is an enduring challenge of reinforcement learning (RL). With the advent of versatile large language models (LLMs), recent works impart common-sense knowledge to accelerate policy learning for RL processes. However, we note that
Externí odkaz:
http://arxiv.org/abs/2407.03964
In real-world scenarios, individuals often cooperate for mutual benefit. However, differences in wealth can lead to varying outcomes for similar actions. In complex social networks, individuals' choices are also influenced by their neighbors. To expl
Externí odkaz:
http://arxiv.org/abs/2407.03904
Autor:
Li, Yi-Chen, Zhang, Fuxiang, Qiu, Wenjie, Yuan, Lei, Jia, Chengxing, Zhang, Zongzhang, Yu, Yang
We consider the problem of adapting Large Language Models (LLMs) pre-trained with Reinforcement Learning from Human Feedback (RLHF) to downstream preference data. Naive approaches to achieve this could be supervised fine-tuning on preferred responses
Externí odkaz:
http://arxiv.org/abs/2407.03856
This paper presents a Genetic Algorithm (GA) designed to reconfigure a large group of modular Unmanned Aerial Vehicles (UAVs), each with different weights and inertia parameters, into an over-actuated flight structure with improved dynamic properties
Externí odkaz:
http://arxiv.org/abs/2407.03724