Sheng Li – författare

The 31 full presented in this volume were carefully reviewed and selected from 90 submissions.

The conference papers covers the following topics such as Machine Translation and Multilingual Information Processing, Minority Language Information Processing, Social Computing and Sentiment Analysis, Text Generation and Summarization, Information Retrieval, Dialogue and Question Answering, Linguistics and Cognitive Science, Language Resource and Evaluation, Knowledge Graph and Information Extraction, and NLP Applications.

Machine Learning for Causal Inference explores the challenges associated with the relationship between machine learning and causal inference, such as biased estimates of causal effects, untrustworthy models, and complicated applications in other artificial intelligence domains. However, it also presents potential solutions to these issues. The book is a valuable resource for researchers, teachers, practitioners, and students interested in these fields. It provides insights into how combining machine learning and causal inference can improve the system''s capability to accomplish causal artificial intelligence based on data. The book showcases promising research directions and emphasizes the importance of understanding the causal relationship to construct different machine-learning models from data.

Signals and systems enjoy wide application in industry and daily life, and understanding basic concepts of the subject area is of importance to undergraduates majoring in engineering. With rigorous mathematical deduction, this introductory text book is helpful for students who study communications engineering, electrical and electronic engineering, and control engineering. Additionally, supplementary materials are provided for self-learners.

Signals and systems enjoy wide application in industry and daily life, and understanding basic concepts of the subject area is of importance to undergraduates majoring in engineering. With rigorous mathematical deduction, this introductory text book is helpful for students who study communications engineering, electrical and electronic engineering, and control engineering. Additionally, supplementary materials are provided for self-learners.

Leveraging the theory of low-rank and sparse modeling, the authors develop robust feature representations under various learning paradigms, including unsupervised learning, supervised learning, semi-supervised learning, multi-view learning, transfer learning, and deep learning. Robust Representations for Data Analytics covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.

This book constitutes the refereed proceedings of the 11th Chinese Conference on Image and Graphics Technologies and Applications, IGTA 2016, held in Beijing, China in July 2016.

The 27 papers presented were carefully reviewed and selected from 69 submissions. They provide a forum for sharing progresses in the areas of image processing technology; image analysis and understanding; computer vision and pattern recognition; big data mining, computer graphics and VR; as well as image technology applications.