hurricane 发表于 2025-3-23 09:41:17
Dynamic Network Embedding by Using Sparse Deep Autoencodercant attention. Almost all existing static network embedding and dynamic network embedding methods that employ deep models adopt dense structures. Deep models can ensure that the network embedding achieves a good effect on the task (link prediction, network reconstruction, etc.); however, all works过剩 发表于 2025-3-23 15:07:52
http://reply.papertrans.cn/17/1621/162079/162079_12.png草率男 发表于 2025-3-23 19:00:57
http://reply.papertrans.cn/17/1621/162079/162079_13.pngExplicate 发表于 2025-3-24 01:48:08
A Novel Nonlinear Dictionary Learning Algorithm Based on Nonlinear-KSVD and Nonlinear-MOD most commonly applied method, and it is typically utilized to address various signal processing problems. However, linear dictionary learning cannot meet the requirements of nonlinear signal processing, and the nonlinear signals cannot be accurately simulated and processed. In this study, we first并入 发表于 2025-3-24 02:24:33
http://reply.papertrans.cn/17/1621/162079/162079_15.png乞讨 发表于 2025-3-24 08:30:02
stance measure, and illustrate the rationality of the method with the analysis and comparison of numerical examples. Finally, the method is applied to practical medical diagnosis. The experimental results show that the new method has certain validity and feasibility.arousal 发表于 2025-3-24 12:18:05
http://reply.papertrans.cn/17/1621/162079/162079_17.pnglicence 发表于 2025-3-24 15:02:27
Conference proceedings 2022igence, held in Beijing, China, in August 2022. CICAI is a summit forum in the field of artificial intelligence and the 2022 forum was hosted by Chinese Association for Artificial Intelligence (CAAI). ..The 164 papers were thoroughly reviewed and selected from 521 submissions. CICAI aims to establis古代 发表于 2025-3-24 20:46:51
http://reply.papertrans.cn/17/1621/162079/162079_19.pngAllergic 发表于 2025-3-25 01:21:58
Adaptive Combination of Filtered-X NLMS and Affine Projection Algorithms for Active Noise Control FxNLMS algorithm and FxAP algorithm, and a coupling factor designed by gradient descent is used to update the filter weights. The simulation experiment results in stationary and nonstationary scenarios demonstrate the better performance of the proposed algorithm as compared with the conventional algorithms.