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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Label Selection Approach to?Learning from?Crowdsd to almost all variants of supervised learning problems by simply adding a selector network and changing the objective function for existing models, without explicitly assuming a model of the noise in crowd annotations. The experimental results show that the performance of the proposed method is al

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Multi-model Smart Contract Vulnerability Detection Based on BiGRUrket, and their security research has attracted much attention in the academic community. Traditional smart contract detection methods rely heavily on expert rules, resulting in low detection precision and efficiency. This paper explores the effectiveness of deep learning methods on smart contract d

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Time-Warp-Invariant Processing with?Multi-spike Learnings both spatial and temporal dimensions. Learning of such a clue information could be challenging, especially considering the case of long-delayed reward. This temporal credit assignment problem has been solved by a new concept of aggregate-label learning that motivates the development of a family of

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Multi-scale Multi-step Dependency Graph Neural Network for?Multivariate Time-Series Forecastingg dependencies between variables and the weak correlation in time-series across different time scales. To overcome these challenges, we proposed a graph neural network-based multi-scale multi-step dependency (GMSSD) model. To capture temporal dependencies in time-series data, we first designed a tem

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