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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Time-Warp-Invariant Processing with?Multi-spike Learningt show the time-warp invariant characteristic of a conductance-based neuron model, based on which we then develop a new multi-spike learning algorithm for time-warp-invariant processing. Experimental results for speech recognition highlight the outstanding robustness of our algorithm against tempora

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ECOST: Enhanced CoST Framework for?Fast and?Accurate Time Series Forecastinged for storing past trend features, improving the learning of trend nuances. Our ECoST model has shown significant improvements, with an increase in prediction accuracy by 8.5% and a 74% enhancement in training time efficiency compared to the CoST model. These results were validated through experime

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LCformer: Linear Convolutional Decomposed Transformer for Long-Term Series Forecastinger consumption on long-term series prediction problems. Experimental results on two different types of benchmark datasets show that the LCformer exhibits better prediction performance compared to those of the state-of-the-art Transformer-based methods, and exhibits near linear complexity for long se

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Reinforcement Learning-Based Consensus Reaching in?Large-Scale Social Networksnd exploitation algorithm based on policy gradient is designed to optimize the model. Based on the reward values in inter-agent interaction process, the agents can adaptively learn the neighbor reweighting strategy with multi-objective trade-off abilities. Extensive simulations demonstrate that the

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