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Stock Type Prediction Model Based on Hierarchical Graph Neural Network
Jianhua Yao;Yuxin Dong;Jiajing Wang;Bingxing Wang;Hongye Zheng;Honglin Qin
2024 International Conference on Electronics and Devices, Computational Science (ICEDCS)
Year: 2024 | Conference Paper |
Cited by: Papers (5)
This paper introduces a novel approach to stock data analysis by employing a Hierarchical Graph Neural Network (HGNN) model that captures multi-level information and relational structures in the stock market. The HGNN model integrates stock relationship data and hierarchical attributes to predict stock types effectively. The paper discusses the construction of a stock industry relationship graph a...Show More
The Synergistic Role of Deep Learning and Neural Architecture Search in Advancing Artificial Intelligence
Xu Yan;Junliang Du;Lun Wang;Yingbin Liang;Jiacheng Hu;Bingxing Wang
2024 International Conference on Electronics and Devices, Computational Science (ICEDCS)
Year: 2024 | Conference Paper |
This paper delves into the significance and interaction between deep learning (DL) and neural architecture search (NAS) within the realm of artificial intelligence. As DL has become integral in addressing complex problems through its robust learning capabilities, NAS offers the potential to autonomously discover efficient neural network structures. The fusion of these technologies not only enhance...Show More
Adaptive Friction in Deep Learning: Enhancing Optimizers with Sigmoid and Tanh Function
Hongye Zheng;Bingxing Wang;Minheng Xiao;Honglin Qin;Zhizhong Wu;Lianghao Tan
2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS)
Year: 2024 | Conference Paper |
Cited by: Papers (8)
Adaptive optimizers are pivotal in guiding the weight updates of deep neural networks, yet they often face challenges such as poor generalization and oscillation issues. To counter these, we introduce sigSignGrad and tanhSignGrad, two novel optimizers that integrate adaptive friction coefficients based on the Sigmoid and Tanh functions, respectively. These algorithms leverage short-term gradient i...Show More
Reducing Bias in Deep Learning Optimization: The RSGDM Approach
Honglin Qin;Hongye Zheng;Bingxing Wang;Zhizhong Wu;Bingyao Liu;Yuanfang Yang
2024 4th International Conference on Computer Science and Blockchain (CCSB)
Year: 2024 | Conference Paper |
Cited by: Papers (6)
Currently, widely used first-order deep learning optimizers include non-adaptive learning rate optimizers and adaptive learning rate optimizers. The former is represented by SGDM (Stochastic Gradient Descent with Momentum), while the latter is represented by Adam. Both of these methods use exponential moving averages to estimate the overall gradient. However, estimating the overall gradient using ...Show More
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