Zobrazeno 1 - 10
of 240
pro vyhledávání: '"Wang, Pengyang"'
Knowledge Tracing aims to assess student learning states by predicting their performance in answering questions. Different from the existing research which utilizes fixed-length learning sequence to obtain the student states and regards KT as a stati
Externí odkaz:
http://arxiv.org/abs/2407.20824
Autor:
Zhou, Yicheng, Wang, Pengfei, Dong, Hao, Zhang, Denghui, Yang, Dingqi, Fu, Yanjie, Wang, Pengyang
Urban traffic speed prediction aims to estimate the future traffic speed for improving urban transportation services. Enormous efforts have been made to exploit Graph Neural Networks (GNNs) for modeling spatial correlations and temporal dependencies
Externí odkaz:
http://arxiv.org/abs/2406.16992
Autor:
Wang, Zaitian, Wang, Pengfei, Liu, Kunpeng, Wang, Pengyang, Fu, Yanjie, Lu, Chang-Tien, Aggarwal, Charu C., Pei, Jian, Zhou, Yuanchun
Data augmentation is a series of techniques that generate high-quality artificial data by manipulating existing data samples. By leveraging data augmentation techniques, AI models can achieve significantly improved applicability in tasks involving sc
Externí odkaz:
http://arxiv.org/abs/2405.09591
Autor:
Ning, Zhiyuan, Tian, Chunlin, Xiao, Meng, Fan, Wei, Wang, Pengyang, Li, Li, Wang, Pengfei, Zhou, Yuanchun
Federated Learning faces significant challenges in statistical and system heterogeneity, along with high energy consumption, necessitating efficient client selection strategies. Traditional approaches, including heuristic and learning-based methods,
Externí odkaz:
http://arxiv.org/abs/2405.06312
Autor:
Deng, Bangchao, Qu, Bingqing, Wang, Pengyang, Yang, Dingqi, Fankhauser, Benjamin, Cudre-Mauroux, Philippe
Location prediction forecasts a user's location based on historical user mobility traces. To tackle the intrinsic sparsity issue of real-world user mobility traces, spatiotemporal contexts have been shown as significantly useful. Existing solutions m
Externí odkaz:
http://arxiv.org/abs/2402.16310
Due to non-stationarity of time series, the distribution shift problem largely hinders the performance of time series forecasting. Existing solutions either fail for the shifts beyond simple statistics or the limited compatibility with forecasting mo
Externí odkaz:
http://arxiv.org/abs/2401.16777
In the realm of human mobility, the decision-making process for selecting the next-visit location is intricately influenced by a trade-off between spatial and temporal constraints, which are reflective of individual needs and preferences. This trade-
Externí odkaz:
http://arxiv.org/abs/2312.15717
Autor:
Yi, Kun, Zhang, Qi, Fan, Wei, He, Hui, Hu, Liang, Wang, Pengyang, An, Ning, Cao, Longbing, Niu, Zhendong
Multivariate time series (MTS) forecasting has shown great importance in numerous industries. Current state-of-the-art graph neural network (GNN)-based forecasting methods usually require both graph networks (e.g., GCN) and temporal networks (e.g., L
Externí odkaz:
http://arxiv.org/abs/2311.06190
Autor:
Yi, Kun, Zhang, Qi, Fan, Wei, Wang, Shoujin, Wang, Pengyang, He, Hui, Lian, Defu, An, Ning, Cao, Longbing, Niu, Zhendong
Time series forecasting has played the key role in different industrial, including finance, traffic, energy, and healthcare domains. While existing literatures have designed many sophisticated architectures based on RNNs, GNNs, or Transformers, anoth
Externí odkaz:
http://arxiv.org/abs/2311.06184
Urban planning, which aims to design feasible land-use configurations for target areas, has become increasingly essential due to the high-speed urbanization process in the modern era. However, the traditional urban planning conducted by human designe
Externí odkaz:
http://arxiv.org/abs/2310.02453