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

ALLY

書(shū)目名稱Neural Information Processing影響因子(影響力)




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




書(shū)目名稱Neural Information Processing網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Neural Information Processing網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Neural Information Processing被引頻次




書(shū)目名稱Neural Information Processing被引頻次學(xué)科排名




書(shū)目名稱Neural Information Processing年度引用




書(shū)目名稱Neural Information Processing年度引用學(xué)科排名




書(shū)目名稱Neural Information Processing讀者反饋




書(shū)目名稱Neural Information Processing讀者反饋學(xué)科排名

不適當(dāng)

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

Flirtatious

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.

pester

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.

尖酸一點(diǎn)

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.

Preserve

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.

Obstruction

MURKY

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.

潔凈

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.

expansive