Zobrazeno 1 - 10
of 176
pro vyhledávání: '"Wen, Qingsong"'
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
Autor:
Zhao, Sinong, Wang, Wenrui, Xu, Hongzuo, Yu, Zhaoyang, Wen, Qingsong, Wang, Gang, Liu, xiaoguang, Pang, Guansong
Identifying anomalies from time series data plays an important role in various fields such as infrastructure security, intelligent operation and maintenance, and space exploration. Current research focuses on detecting the anomalies after they occur,
Externí odkaz:
http://arxiv.org/abs/2410.12206
Autor:
Li, Zhe, Qiu, Xiangfei, Chen, Peng, Wang, Yihang, Cheng, Hanyin, Shu, Yang, Hu, Jilin, Guo, Chenjuan, Zhou, Aoying, Wen, Qingsong, Jensen, Christian S., Yang, Bin
Time Series Forecasting (TSF) is key functionality in numerous fields, including in finance, weather services, and energy management. While TSF methods are emerging these days, many of them require domain-specific data collection and model training a
Externí odkaz:
http://arxiv.org/abs/2410.11802
In various scientific and engineering fields, the primary research areas have revolved around physics-based dynamical systems modeling and data-driven time series analysis. According to the embedding theory, dynamical systems and time series can be m
Externí odkaz:
http://arxiv.org/abs/2410.06651
Time series analysis is widely used in many fields such as power energy, economics, and transportation, including different tasks such as forecasting, anomaly detection, classification, etc. Missing values are widely observed in these tasks, and ofte
Externí odkaz:
http://arxiv.org/abs/2410.06652
Autor:
Yan, Yibo, Wang, Shen, Huo, Jiahao, Li, Hang, Li, Boyan, Su, Jiamin, Gao, Xiong, Zhang, Yi-Fan, Xu, Tianlong, Chu, Zhendong, Zhong, Aoxiao, Wang, Kun, Xiong, Hui, Yu, Philip S., Hu, Xuming, Wen, Qingsong
As the field of Multimodal Large Language Models (MLLMs) continues to evolve, their potential to revolutionize artificial intelligence is particularly promising, especially in addressing mathematical reasoning tasks. Current mathematical benchmarks p
Externí odkaz:
http://arxiv.org/abs/2410.04509
Autor:
Yu, Miao, Mao, Junyuan, Zhang, Guibin, Ye, Jingheng, Fang, Junfeng, Zhong, Aoxiao, Liu, Yang, Liang, Yuxuan, Wang, Kun, Wen, Qingsong
Research into the external behaviors and internal mechanisms of large language models (LLMs) has shown promise in addressing complex tasks in the physical world. Studies suggest that powerful LLMs, like GPT-4, are beginning to exhibit human-like cogn
Externí odkaz:
http://arxiv.org/abs/2410.01677
Autor:
Qin, Dalin, Li, Yehui, Chen, Weiqi, Zhu, Zhaoyang, Wen, Qingsong, Sun, Liang, Pinson, Pierre, Wang, Yi
Complex distribution shifts are the main obstacle to achieving accurate long-term time series forecasting. Several efforts have been conducted to capture the distribution characteristics and propose adaptive normalization techniques to alleviate the
Externí odkaz:
http://arxiv.org/abs/2409.19718
Deep learning for time series forecasting has seen significant advancements over the past decades. However, despite the success of large-scale pre-training in language and vision domains, pre-trained time series models remain limited in scale and ope
Externí odkaz:
http://arxiv.org/abs/2409.16040
Students frequently make mistakes while solving mathematical problems, and traditional error correction methods are both time-consuming and labor-intensive. This paper introduces an innovative \textbf{V}irtual \textbf{A}I \textbf{T}eacher system desi
Externí odkaz:
http://arxiv.org/abs/2409.09403