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DEWP: Deep Expansion Learning for Wind Power Forecasting.

, , , , , and . ACM Trans. Knowl. Discov. Data, 18 (3): 71:1-71:21 (April 2024)

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Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures., , , , , , and . CoRR, (2022)Learning Intra- and Inter-Cell Differences for Accurate Battery Lifespan Prediction across Diverse Conditions., , , , , , and . CoRR, (2023)A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization., , , , and . J. Mach. Learn. Res., (2017)Efficient Graph Collaborative Filtering with Multi-layer Output-Enhanced Contrastive Learning., , , , , and . ADMA (1), volume 14176 of Lecture Notes in Computer Science, page 755-771. Springer, (2023)Learning Differential Operators for Interpretable Time Series Modeling., , , , , and . KDD, page 1192-1201. ACM, (2022)DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting., , , , , , and . ICLR, OpenReview.net, (2022)DEWP: Deep Expansion Learning for Wind Power Forecasting., , , , , and . CoRR, (2024)A continuous glucose monitoring measurements forecasting approach via sporadic blood glucose monitoring., , , , , , and . BIBM, page 860-863. IEEE, (2022)Towards Foundation Models for Learning on Tabular Data., , , , and . CoRR, (2023)Revisiting the Evaluation of End-to-end Event Extraction., , , and . ACL/IJCNLP (Findings), volume ACL/IJCNLP 2021 of Findings of ACL, page 4609-4617. Association for Computational Linguistics, (2021)