Zobrazeno 1 - 10
of 54
pro vyhledávání: '"Nie, Yuqi"'
Collaborative inference among multiple wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications, particularly for sensing and computer vision. This approach typically involves a three-stage process: a
Externí odkaz:
http://arxiv.org/abs/2410.19917
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
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:
Nie, Yuqi, Kong, Yaxuan, Dong, Xiaowen, Mulvey, John M., Poor, H. Vincent, Wen, Qingsong, Zohren, Stefan
Recent advances in large language models (LLMs) have unlocked novel opportunities for machine learning applications in the financial domain. These models have demonstrated remarkable capabilities in understanding context, processing vast amounts of d
Externí odkaz:
http://arxiv.org/abs/2406.11903
Collaborative inference in next-generation networks can enhance Artificial Intelligence (AI) applications, including autonomous driving, personal identification, and activity classification. This method involves a three-stage process: a) data acquisi
Externí odkaz:
http://arxiv.org/abs/2406.00256
Mobility analysis is a crucial element in the research area of transportation systems. Forecasting traffic information offers a viable solution to address the conflict between increasing transportation demands and the limitations of transportation in
Externí odkaz:
http://arxiv.org/abs/2405.02357
Autor:
Liang, Yuxuan, Wen, Haomin, Nie, Yuqi, Jiang, Yushan, Jin, Ming, Song, Dongjin, Pan, Shirui, Wen, Qingsong
Time series analysis stands as a focal point within the data mining community, serving as a cornerstone for extracting valuable insights crucial to a myriad of real-world applications. Recent advances in Foundation Models (FMs) have fundamentally res
Externí odkaz:
http://arxiv.org/abs/2403.14735
The criticality of prompt and precise traffic forecasting in optimizing traffic flow management in Intelligent Transportation Systems (ITS) has drawn substantial scholarly focus. Spatio-Temporal Graph Neural Networks (STGNNs) have been lauded for the
Externí odkaz:
http://arxiv.org/abs/2308.07496
We propose an efficient design of Transformer-based models for multivariate time series forecasting and self-supervised representation learning. It is based on two key components: (i) segmentation of time series into subseries-level patches which are
Externí odkaz:
http://arxiv.org/abs/2211.14730
We find a heterogeneity in both complex and real valued neural networks with the insight from wave optics, claiming a much more important role of phase than its amplitude counterpart in the weight matrix. In complex-valued neural networks, we show th
Externí odkaz:
http://arxiv.org/abs/2111.02014