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Titlebook: Neural Information Processing; 30th International C Biao Luo,Long Cheng,Chaojie Li Conference proceedings 2024 The Editor(s) (if applicable

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樓主
發(fā)表于 2025-3-21 18:11:41 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Neural Information Processing
副標題30th International C
編輯Biao Luo,Long Cheng,Chaojie Li
視頻videohttp://file.papertrans.cn/664/663596/663596.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Neural Information Processing; 30th International C Biao Luo,Long Cheng,Chaojie Li Conference proceedings 2024 The Editor(s) (if applicable
描述The six-volume set LNCS 14447 until 14452 constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023.?.The 652 papers presented in the proceedings set were carefully reviewed and selected from 1274 submissions. They focus on theory and algorithms, cognitive neurosciences; human centred computing; applications in neuroscience, neural networks, deep learning, and related fields.?.
出版日期Conference proceedings 2024
關(guān)鍵詞pattern recognition; affective and cognitive learning; big data; bioinformatics; brain-machine interface
版次1
doihttps://doi.org/10.1007/978-981-99-8079-6
isbn_softcover978-981-99-8078-9
isbn_ebook978-981-99-8079-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

書目名稱Neural Information Processing影響因子(影響力)




書目名稱Neural Information Processing影響因子(影響力)學(xué)科排名




書目名稱Neural Information Processing網(wǎng)絡(luò)公開度




書目名稱Neural Information Processing網(wǎng)絡(luò)公開度學(xué)科排名




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發(fā)表于 2025-3-21 22:09:35 | 只看該作者
Conference proceedings 2024ICONIP 2023, held in Changsha, China, in November 2023.?.The 652 papers presented in the proceedings set were carefully reviewed and selected from 1274 submissions. They focus on theory and algorithms, cognitive neurosciences; human centred computing; applications in neuroscience, neural networks, d
板凳
發(fā)表于 2025-3-22 02:11:38 | 只看該作者
Nonlinear Multiple-Delay Feedback Based Kernel Least Mean Square Algorithmn-square convergence analyses is also conducted. Simulation results under chaotic time-series prediction and real-world data applications show that NMDF-KLMS achieves a faster convergence rate and superior filtering accuracy.
地板
發(fā)表于 2025-3-22 05:50:51 | 只看該作者
Integrated Design of?Fully Distributed Adaptive State Estimation and?Consensus Control for?Multi-agen for multi-agent systems is designed to ensure the agents achieve consensus. The proposed control input of each agent relies on its own estimation of the entire state. Theoretical analysis proves the effectiveness of the algorithm and practical applications are given by simulation.
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發(fā)表于 2025-3-22 09:58:03 | 只看該作者
Learning Adaptable Risk-Sensitive Policies to?Coordinate in?Multi-agent General-Sum Gamesiary opponent modeling task to infer opponents’ types and dynamically alter corresponding strategies during execution. Extensive experiments show that ARSP agents can achieve stable coordination during training and adapt to non-cooperative opponents during execution, outperforming a set of baselines by a large margin.
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發(fā)表于 2025-3-22 15:41:57 | 只看該作者
Traffic Data Recovery and?Outlier Detection Based on?Non-negative Matrix Factorization and?Truncatedatic loss function. Although the objective function in our model is non-convex and non-smooth, we convert it to a convex formulation using half-quadratic theory. Then, a solver based on block coordinate descent is developed. Our experiments on real-world traffic datasets demonstrate superior performance compared to state-of-the-art methods.
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Amortized Variational Inference via Nosé-Hoover Thermostat Hamiltonian Monte Carloeters of the inference distribution. The proposed method improves variational inference accuracy for the latent by subtly dealing with the noise introduced by stochastic gradient without estimating that noise explicitly. Experiments benchmarking our method against baseline generative methods highlight the effectiveness of our proposed method.
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發(fā)表于 2025-3-23 03:01:53 | 只看該作者
Distributed State Estimation for?Multi-agent Systems Under Consensus Controlng the whole process. The theoretical analysis demonstrates that the realization of distributed output tracking and state estimation. Moreover, all agents achieve consensus. Finally, numerical simulations are worked out to show the effectiveness of the proposed algorithm.
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