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Correction to: Efficient methods for finding an optimal network location for travel planning.

, and . J. Supercomput., 77 (11): 12581 (2021)

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Efficient methods for finding an optimal network location for travel planning., and . J. Supercomput., 77 (11): 12561-12580 (2021)Correction to: Efficient methods for finding an optimal network location for travel planning., and . J. Supercomput., 77 (11): 12581 (2021)Energy-Efficient Iterative Refinement Using Dynamic Precision., , , , and . IEEE J. Emerg. Sel. Topics Circuits Syst., 8 (4): 722-735 (2018)Partially Observable Hierarchical Reinforcement Learning with AI Planning (Student Abstract)., , , , and . AAAI, page 23635-23636. AAAI Press, (2024)The Effects of Weight Quantization on Online Federated Learning for the IoT: A Case Study., , , and . IEEE Access, (2024)On the efficacy of per-relation basis performance evaluation for PPI extraction and a high-precision rule-based approach., , , , and . BMC Medical Informatics Decis. Mak., 13 (S-1): S7 (2013)Resource Capacitated Collective Travel Planning in Spatial Databases., and . IEEE Access, (2020)A case study on latency, bandwidth and energy efficiency of mobile 5G and YouTube Edge service in London. Why the 5G ecosystem and energy efficiency matter?, , and . CoRR, (2023)A Weighted Mini-Bucket Bound for Solving Influence Diagram., , , and . UAI, volume 115 of Proceedings of Machine Learning Research, page 1159-1168. AUAI Press, (2019)Resource-Efficient Deep Learning: A Survey on Model-, Arithmetic-, and Implementation-Level Techniques., , , , , , , , and . CoRR, (2021)