Zobrazeno 1 - 10
of 163
pro vyhledávání: '"Wen, Qingsong"'
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
Unsupervised domain adaptation (UDA) of time series aims to teach models to identify consistent patterns across various temporal scenarios, disregarding domain-specific differences, which can maintain their predictive accuracy and effectively adapt t
Externí odkaz:
http://arxiv.org/abs/2409.12169
Knowledge tagging for questions is vital in modern intelligent educational applications, including learning progress diagnosis, practice question recommendations, and course content organization. Traditionally, these annotations have been performed b
Externí odkaz:
http://arxiv.org/abs/2409.08406
Autor:
Mao, Shengzhong, Zhang, Chaoli, Song, Yichi, Wang, Jindong, Zeng, Xiao-Jun, Xu, Zenglin, Wen, Qingsong
Recent advancements in the collection and analysis of sequential educational data have brought time series analysis to a pivotal position in educational research, highlighting its essential role in facilitating data-driven decision-making. However, t
Externí odkaz:
http://arxiv.org/abs/2408.13960
Autor:
Zhong, Aoxiao, Mo, Dengyao, Liu, Guiyang, Liu, Jinbu, Lu, Qingda, Zhou, Qi, Wu, Jiesheng, Li, Quanzheng, Wen, Qingsong
Logs are ubiquitous digital footprints, playing an indispensable role in system diagnostics, security analysis, and performance optimization. The extraction of actionable insights from logs is critically dependent on the log parsing process, which co
Externí odkaz:
http://arxiv.org/abs/2408.13727
Autor:
Zhang, Yi-Fan, Zhang, Huanyu, Tian, Haochen, Fu, Chaoyou, Zhang, Shuangqing, Wu, Junfei, Li, Feng, Wang, Kun, Wen, Qingsong, Zhang, Zhang, Wang, Liang, Jin, Rong, Tan, Tieniu
Comprehensive evaluation of Multimodal Large Language Models (MLLMs) has recently garnered widespread attention in the research community. However, we observe that existing benchmarks present several common barriers that make it difficult to measure
Externí odkaz:
http://arxiv.org/abs/2408.13257
Autor:
Zhang, Xuanwang, Song, Yunze, Wang, Yidong, Tang, Shuyun, Li, Xinfeng, Zeng, Zhengran, Wu, Zhen, Ye, Wei, Xu, Wenyuan, Zhang, Yue, Dai, Xinyu, Zhang, Shikun, Wen, Qingsong
Large Language Models (LLMs) demonstrate human-level capabilities in dialogue, reasoning, and knowledge retention. However, even the most advanced LLMs face challenges such as hallucinations and real-time updating of their knowledge. Current research
Externí odkaz:
http://arxiv.org/abs/2408.11381
Autor:
Kong, Yaxuan, Wang, Zepu, Nie, Yuqi, Zhou, Tian, Zohren, Stefan, Liang, Yuxuan, Sun, Peng, Wen, Qingsong
Traditional recurrent neural network architectures, such as long short-term memory neural networks (LSTM), have historically held a prominent role in time series forecasting (TSF) tasks. While the recently introduced sLSTM for Natural Language Proces
Externí odkaz:
http://arxiv.org/abs/2408.10006
Autor:
Chen, Feiyi, Zhang, Yingying, Fan, Lunting, Liang, Yuxuan, Pang, Guansong, Wen, Qingsong, Deng, Shuiguang
Slow task detection is a critical problem in cloud operation and maintenance since it is highly related to user experience and can bring substantial liquidated damages. Most anomaly detection methods detect it from a single-task aspect. However, cons
Externí odkaz:
http://arxiv.org/abs/2408.04236