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Full-Text Articles in Physical Sciences and Mathematics
Neural Generative Models And Representation Learning For Information Retrieval, Qingyao Ai
Neural Generative Models And Representation Learning For Information Retrieval, Qingyao Ai
Doctoral Dissertations
Information Retrieval (IR) concerns about the structure, analysis, organization, storage, and retrieval of information. Among different retrieval models proposed in the past decades, generative retrieval models, especially those under the statistical probabilistic framework, are one of the most popular techniques that have been widely applied to Information Retrieval problems. While they are famous for their well-grounded theory and good empirical performance in text retrieval, their applications in IR are often limited by their complexity and low extendability in the modeling of high-dimensional information. Recently, advances in deep learning techniques provide new opportunities for representation learning and generative models for information …
Deep-Learned Generative Representations Of 3d Shape Families, Haibin Huang
Deep-Learned Generative Representations Of 3d Shape Families, Haibin Huang
Doctoral Dissertations
Digital representations of 3D shapes are becoming increasingly useful in several emerging applications, such as 3D printing, virtual reality and augmented reality. However, traditional modeling softwares require users to have extensive modeling experience, artistic skills and training to handle their complex interfaces and perform the necessary low-level geometric manipulation commands. Thus, there is an emerging need for computer algorithms that help novice and casual users to quickly and easily generate 3D content. In this work, I will present deep learning algorithms that are capable of automatically inferring parametric representations of shape families, which can be used to generate new 3D …