Graph neural networks and attention-based CNN-LSTM for protein classification

1 code implementation • 20 Apr 2022 • Zhuangwei Shi, Bo Li

This paper proposes three models for protein classification.

Graph Attention Graph Classification +1

Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction

1 code implementation • 6 Apr 2022 • Zhuangwei Shi, Yang Hu, Guangliang Mo, Jian Wu

Due to the complex volatility of the stock market, the research and prediction on the change of the stock price, can avoid the risk for the investors.

Stock Prediction Time Series +1

Differential equation and probability inspired graph neural networks for latent variable learning

1 code implementation • 28 Feb 2022 • Zhuangwei Shi

Probabilistic theory and differential equation are powerful tools for the interpretability and guidance of the design of machine learning models, especially for illuminating the mathematical motivation of learning latent variable from observation.

Stock Prediction text-classification +2

Bidirectional LSTM-CRF Attention-based Model for Chinese Word Segmentation

no code implementations • 20 May 2021 • Chen Jin, Zhuangwei Shi, Weihua Li, Yanbu Guo

Chinese word segmentation (CWS) is the basic of Chinese natural language processing (NLP).

Chinese Word Segmentation Segmentation

Incorporating Transformer and LSTM to Kalman Filter with EM algorithm for state estimation

1 code implementation • 1 May 2021 • Zhuangwei Shi

Kalman Filter requires the true parameters of the model and solves optimal state estimation recursively.