42 citations · 65 across the 6 of their papers we have counts for
6 papers
SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting
Shengsheng Lin, Weiwei Lin, Wentai Wu +3
RNN-based methods have faced challenges in the Long-term Time Series Forecasting (LTSF) domain when dealing with excessively long look-back windows and forecast horizons. Consequen…
PETformer: Long-term Time Series Forecasting via Placeholder-enhanced Transformer
Shengsheng Lin, Weiwei Lin, Wentai Wu +2
Recently, the superiority of Transformer for long-term time series forecasting (LTSF) tasks has been challenged, particularly since recent work has shown that simple models can out…
Fusion of Global and Local Knowledge for Personalized Federated Learning
Tiansheng Huang, Li Shen, Yan Sun +2
Personalized federated learning, as a variant of federated learning, trains customized models for clients using their heterogeneously distributed data. However, it is still inconcl…
Achieving Personalized Federated Learning with Sparse Local Models
Tiansheng Huang, Shiwei Liu, Li Shen +3
Federated learning (FL) is vulnerable to heterogeneously distributed data, since a common global model in FL may not adapt to the heterogeneous data distribution of each user. To c…
Variation-Incentive Loss Re-weighting for Regression Analysis on Biased Data
Wentai Wu, Ligang He, Weiwei Lin
Both classification and regression tasks are susceptible to the biased distribution of training data. However, existing approaches are focused on the class-imbalanced learning and…
FedProf: Selective Federated Learning with Representation Profiling
Wentai Wu, Ligang He, Weiwei Lin +1
Federated Learning (FL) has shown great potential as a privacy-preserving solution to learning from decentralized data that are only accessible to end devices (i.e., clients). In m…