relieve
发表于 2025-3-25 05:45:03
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eucalyptus
发表于 2025-3-25 08:10:05
Kommunikationsziele: Kosten und Nutzen address the curse of dimensionality, we decompose the joint optimization of content caching and delivery into the vehicle side and RSU side subproblems. DDPG and Double-DQN are applied to address these two subtasks. Furthermore, an LSTM-based location prediction module is built to mine the mobility
Cantankerous
发表于 2025-3-25 13:25:48
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NAG
发表于 2025-3-25 19:19:22
Johann Wolfgang Goethe,Maximen,Reflexionenriments. Compared with previous approaches, the experimental results show that SeqTrace achieves API-call sequence tracing with fine-grained semantic information and reduces the tracing overhead by more than 80%.
一起平行
发表于 2025-3-25 21:12:24
Johann Wolfgang Goethe,Maximen,Reflexionenuced training data on the target platform, and finally obtains a cross-platform model. We validate the effectiveness of . using a group of benchmarks on X86- and ARM-based platforms that use three different types of commonly used processors. Evaluation results show that when applying ., only 1% of t
discord
发表于 2025-3-26 01:36:42
Kommunikationsziele: Kosten und Nutzenuence length adaptation method for offline training, and a sliding window inference strategy for online inference. Finally, we build the system prototype and give an extensive performance evaluation in real-world traffic environments. The experimental results conclusively demonstrate the feasibility
Mri485
发表于 2025-3-26 05:11:49
R. Kraschutzki (Tresor-Kassirer) Compared to other exterior knowledge, statistical features are naturally compatible with Web service recommendation tasks. To fully exploit the statistical features, this paper proposes a Web service recommendation method based on adaptive gate network and xDeepFM model. In this method, firstly, th
内疚
发表于 2025-3-26 09:18:08
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Individual
发表于 2025-3-26 15:03:31
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串通
发表于 2025-3-26 20:38:28
https://doi.org/10.1007/978-3-662-39615-5ques are adopted to emphasize the sample tail to improve fitting accuracy. The prototype of . is implemented with GraphX on Spark, and is evaluated against various well-known graphs. The experimental results show that . is up to 10x faster than accurate counting, keeping the same error level below 1