Time series forecasting (TSF) refers to a fundamental task of predicting future sequential data based on historical observations. One representative category of TSF methods is transformer-based approaches, which translate time series into token sequences (i.e., as raw texts) before applying well-established transformer...
Yiming Wang, Yiqin Su, Xi-Ming Li et al.· Proceedings of the Thirty-Fi...· 0 citations
EoupCT effectively preserves both task-specific proficiency and inherent general-purpose knowledge, successfully mitigating the catastrophic forgetting, and forms a multi-objective optimization problem and introduces a first-order efficient Pareto optimizer.
Bing Wang, C. Li, Xin-Qiang Cai et al.· 1 citation
REHC-PML (Label Confidence REcovery with High-order Label Correlations in Partial Multi-label Learning), which mines frequent high-order co-occurrences from candidate sets to identify global high-order label correlations, selects instance-relevant correlations via Gumbel-Softmax pruning, and propagates their evidence t...
Yuanchao Dai, Xi-Ming Li, Ming-Kun Xie et al.· Proceedings of the Thirty-Fi...· 0 citations
A novel PML method, namely Wasserstein Partial Multi-Label Learning with dual Label Correlation Perspectives (Wpml3cp), solved by the gradient descent with an augmented Lagrange multiplier technique, and empirical results demonstrate that Wpml3cp and Wpml3cp-D can outperform the PML baselines in various noisy levels.
Xi-Ming Li, Yuanchao Dai, Bing Wang et al.· ACM Transactions on Knowledg...· 0 citations
A comprehensive study to investigate the basic characteristics of current Positive-Unlabeled learning methods and proposes a general framework of PU learning by integrating the set-aware empirical risk with pseudo-labeling.
Yuanchao Dai, Zhengzhang Hou, C. Li et al.· Neural Information Processin...· 2 citations
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