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Characterizing, modeling, and improving the QoE of mobile devices with low battery level.

, , and . MICRO, page 713-724. ACM, (2015)

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Class Imbalance Data-Generation for Software Defect Prediction., , , and . APSEC, page 276-283. IEEE, (2019)Randomness in Neural Network Training: Characterizing the Impact of Tooling., , , and . MLSys, mlsys.org, (2022)MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning., , , , , , , and . ACL (Findings), page 599-621. Association for Computational Linguistics, (2024)A Static Model of a Notched Continuum Robot with Non-Constant Curvature for Minimally Invasive Surgery: Base on Taylor Expansion Approach., , , , , and . ICARM, page 1006-1010. IEEE, (2023)Characterizing, modeling, and improving the QoE of mobile devices with low battery level., , and . MICRO, page 713-724. ACM, (2015)Shift-BNN: Highly-Efficient Probabilistic Bayesian Neural Network Training via Memory-Friendly Pattern Retrieving., , , , , and . MICRO, page 885-897. ACM, (2021)Coherent Entity Disambiguation via Modeling Topic and Categorical Dependency., , , , , and . EMNLP (Findings), page 7480-7492. Association for Computational Linguistics, (2023)Patient-Trial Matching with Deep Embedding and Entailment Prediction., , , and . WWW, page 1029-1037. ACM / IW3C2, (2020)Toward Customized Hybrid Fuel-Cell and Battery-powered Mobile Device for Individual Users., , , , , , and . ACM Trans. Embed. Comput. Syst., 18 (6): 123:1-123:20 (2020)MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning., , , , , , and . CoRR, (2023)