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TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
Haixu Wu, Tengge Hu, Yong Liu +3
Time series analysis is of immense importance in extensive applications, such as weather forecasting, anomaly detection, and action recognition. This paper focuses on temporal vari…
Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models
Yang Shu, Zhangjie Cao, Ziyang Zhang +2
Transfer learning aims to leverage knowledge from pre-trained models to benefit the target task. Prior transfer learning work mainly transfers from a single model. However, with th…
Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting
Yong Liu, Haixu Wu, Jianmin Wang +1
Transformers have shown great power in time series forecasting due to their global-range modeling ability. However, their performance can degenerate terribly on non-stationary real…
MetaSets: Meta-Learning on Point Sets for Generalizable Representations
Chao Huang, Zhangjie Cao, Yunbo Wang +2
Deep learning techniques for point clouds have achieved strong performance on a range of 3D vision tasks. However, it is costly to annotate large-scale point sets, making it critic…
From Big to Small: Adaptive Learning to Partial-Set Domains
Zhangjie Cao, Kaichao You, Ziyang Zhang +2
Domain adaptation targets at knowledge acquisition and dissemination from a labeled source domain to an unlabeled target domain under distribution shift. Still, the common requirem…
Debiased Self-Training for Semi-Supervised Learning
Baixu Chen, Junguang Jiang, Ximei Wang +3
Deep neural networks achieve remarkable performances on a wide range of tasks with the aid of large-scale labeled datasets. Yet these datasets are time-consuming and labor-exhausti…