Zhuo Gong

Qianying Liu, Zhuo Gong, Zhengdong Yang et al.

Low-resource speech recognition has been long-suffering from insufficient training data. In this paper, we propose an approach that leverages neighboring languages to improve low-resource scenario performance, founded on the hypothesis that similar linguistic units in neighboring languages exhibit comparable term frequency distributions, which enables us to construct a Huffman tree for performing multilingual hierarchical Softmax decoding. This hierarchical structure enables cross-lingual knowledge sharing among similar tokens, thereby enhancing low-resource training outcomes. Empirical analyses demonstrate that our method is effective in improving the accuracy and efficiency of low-resource speech recognition.

Jieyue He, Xinxing Yang, Zhuo Gong et al.

Drug repositioning is designed to discover new uses of known drugs, which is an important and efficient method of drug discovery. Researchers only use one certain type of Collaborative Filtering (CF) models for drug repositioning currently, like the neighborhood based approaches which are good at mining the local information contained in few strong drug-disease associations, or the latent factor based models which are effectively capture the global information shared by a majority of drug-disease associations. Few researchers have combined these two types of CF models to derive a hybrid model with the advantages of both of them. Besides, the cold start problem has always been a major challenge in the field of computational drug repositioning, which restricts the inference ability of relevant models. Inspired by the memory network, we propose the Hybrid Attentional Memory Network (HAMN) model, a deep architecture combines two classes of CF model in a nonlinear manner. Firstly, the memory unit and the attention mechanism are combined to generate the neighborhood contribution representation to capture the local structure of few strong drug-disease associations. Then a variant version of the autoencoder is used to extract the latent factor of drugs and diseases to capture the overall information shared by a majority of drug-disease associations. In that process, ancillary information of drugs and diseases can help to alleviate the cold start problem. Finally, in the prediction stage, the neighborhood contribution representation is combined with the drug latent factor and disease latent factor to produce the predicted value. Comprehensive experimental results on two real data sets show that our proposed HAMN model is superior to other comparison models according to the AUC, AUPR and HR indicators.