Harbin Institute of Technology Shenzhen Graduate School

About: Harbin Institute of Technology Shenzhen Graduate School is a based out in . It is known for research contribution in the topics: Feature extraction & Robot. The organization has 2496 authors who have published 2434 publications receiving 43188 citations.

Convolutional Neural Network Architectures for Matching Natural Language Sentences

Abstract: Semantic matching is of central importance to many natural language tasks [2,28]. A successful matching algorithm needs to adequately model the internal structures of language objects and the interaction between them. As a step toward this goal, we propose convolutional neural network models for matching two sentences, by adapting the convolutional strategy in vision and speech. The proposed models not only nicely represent the hierarchical structures of sentences with their layer-by-layer composition and pooling, but also capture the rich matching patterns at different levels. Our models are rather generic, requiring no prior knowledge on language, and can hence be applied to matching tasks of different nature and in different languages. The empirical study on a variety of matching tasks demonstrates the efficacy of the proposed model on a variety of matching tasks and its superiority to competitor models.

Convolutional Neural Network Architectures for Matching Natural Language Sentences

Abstract: Semantic matching is of central importance to many natural language tasks \cite{bordes2014semantic,RetrievalQA}. A successful matching algorithm needs to adequately model the internal structures of language objects and the interaction between them. As a step toward this goal, we propose convolutional neural network models for matching two sentences, by adapting the convolutional strategy in vision and speech. The proposed models not only nicely represent the hierarchical structures of sentences with their layer-by-layer composition and pooling, but also capture the rich matching patterns at different levels. Our models are rather generic, requiring no prior knowledge on language, and can hence be applied to matching tasks of different nature and in different languages. The empirical study on a variety of matching tasks demonstrates the efficacy of the proposed model on a variety of matching tasks and its superiority to competitor models.

Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences

Abstract: With the avalanche of biological sequences generated in the post-genomic age, one of the most challenging problems in computational biology is how to effectively formulate the sequence of a biological sample (such as DNA, RNA or protein) with a discrete model or a vector that can effectively reflect its sequence pattern information or capture its key features concerned. Although several web servers and stand-alone tools were developed to address this problem, all these tools, however, can only handle one type of samples. Furthermore, the number of their built-in properties is limited, and hence it is often difficult for users to formulate the biological sequences according to their desired features or properties. In this article, with a much larger number of built-in properties, we are to propose a much more flexible web server called Pse-in-One (http://bioinformatics.hitsz.edu.cn/Pse-in-One/), which can, through its 28 different modes, generate nearly all the possible feature vectors for DNA, RNA and protein sequences. Particularly, it can also generate those feature vectors with the properties defined by users themselves. These feature vectors can be easily combined with machine-learning algorithms to develop computational predictors and analysis methods for various tasks in bioinformatics and system biology. It is anticipated that the Pse-in-One web server will become a very useful tool in computational proteomics, genomics, as well as biological sequence analysis. Moreover, to maximize users’ convenience, its stand-alone version can also be downloaded from http://bioinformatics.hitsz.edu.cn/Pse-in-One/download/, and directly run on Windows, Linux, Unix and Mac OS.

Deep Model Based Domain Adaptation for Fault Diagnosis

Abstract: In recent years, machine learning techniques have been widely used to solve many problems for fault diagnosis. However, in many real-world fault diagnosis applications, the distribution of the source domain data (on which the model is trained) is different from the distribution of the target domain data (where the learned model is actually deployed), which leads to performance degradation. In this paper, we introduce domain adaptation, which can find the solution to this problem by adapting the classifier or the regression model trained in a source domain for use in a different but related target domain. In particular, we proposed a novel deep neural network model with domain adaptation for fault diagnosis. Two main contributions are concluded by comparing to the previous works: first, the proposed model can utilize domain adaptation meanwhile strengthening the representative information of the original data, so that a high classification accuracy in the target domain can be achieved, and second, we proposed several strategies to explore the optimal hyperparameters of the model. Experimental results, on several real-world datasets, demonstrate the effectiveness and the reliability of both the proposed model and the exploring strategies for the parameters.