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Titlebook: Benchmarking, Measuring, and Optimizing; Second BenchCouncil Wanling Gao,Jianfeng Zhan,Dan Stanzione Conference proceedings 2020 Springer

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11#
發(fā)表于 2025-3-23 10:15:38 | 只看該作者
The Implementation and Optimization of Matrix Decomposition Based Collaborative Filtering Task on X8from the big dataset in daily lives. Collaborative filtering is a popular technology often used in recommendation systems, which recommend items to users according to other users having the similar behaviors with the target user or according to the items having the alike properties with the target i
12#
發(fā)表于 2025-3-23 16:32:12 | 只看該作者
An Efficient Implementation of the ALS-WR Algorithm on x86 CPUsvies, games, online shopping, and so on, to solve information redundancy and effectively to recommend interesting products for users. In this paper, we implement and accelerate the Alternating-Least-Squares with Weighted-.-Regularization (ALS-WR) by adopting a two-level parallel strategies on the x8
13#
發(fā)表于 2025-3-23 19:34:00 | 只看該作者
Accelerating Parallel ALS for?Collaborative Filtering on Hadoopsed in CF models to calculate the latent factor matrix factorization. Parallel ALS on Hadoop is widely used in the era of big data. However, existing work on the computational efficiency of parallel ALS on Hadoop have two defects. One is the imbalance of data distribution, the other is lacking the f
14#
發(fā)表于 2025-3-23 22:23:34 | 只看該作者
Improving RGB-D Face Recognition via Transfer Learning from a Pretrained 2D Networktive to variations in poses, facial expressions and illuminations. Depth images provide valuable information to help model facial boundaries and understand the global facial layout and provide low frequency patterns. Intuitively, RGB-D images are more robust to external environments than RGB images.
15#
發(fā)表于 2025-3-24 03:21:49 | 只看該作者
16#
發(fā)表于 2025-3-24 06:36:45 | 只看該作者
17#
發(fā)表于 2025-3-24 13:03:25 | 只看該作者
18#
發(fā)表于 2025-3-24 16:47:37 | 只看該作者
RVTensor: A Light-Weight Neural Network Inference Framework Based on the RISC-V Architecturepose RVTensor that a light-weight neural network inference framework based on the RISC-V architecture. RVTensor is based on the SERVE.r platform and is optimized for resource-poor scenarios. Our experiments demonstrate that the accuracy of RVTensor and the Keras is the same.
19#
發(fā)表于 2025-3-24 19:03:10 | 只看該作者
20#
發(fā)表于 2025-3-24 23:30:15 | 只看該作者
0302-9743 IBench; AI Challenges on X86 using AIBench; AI Challenges on 3D Face Recognition using AIBench; Benchmark; AI and Edge; Big Data; Datacenter; Performance Analysis; Scientific Computing..978-3-030-49555-8978-3-030-49556-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
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