This book equips readers to handle complex multi-view data representation, centered around several major visual applications, sharing many tips and insights through a unified learning framework. This framework is able to model most existing multi-vie
This book provides a definition and study of a knowledge representation and reasoning formalism stemming from conceptual graphs, while focusing on the computational properties of this formalism.
Abstract—The goal of knowledge representation learning is to embed entities and relations into a low-dimensional, continuous vector space. How to push a model to its limit and obtain better results is of great significance in knowledge graphs appli
Neural language representation models such as BERT pre-trained on large-scale corpora can well capture rich semantic patterns from plaintext,andbefine-tunedtoconsistentlyimprove the performance of various NLP tasks. However, the existing pre-trained
Knowledge representation learning (KRL) aims to represent entities and relations in knowledge graph in low-dimensional semantic space, which have been widely used in massive knowledge-driven tasks. In this article, we introduce the reader to the mot
比较新的对比度增强论文，论文《CONTRAST ENHANCEMENT BASED ON LAYERED DIFFERENCE REPRESENTATION》是一种思路比较新颖的利用直方图思想来进行对比度增强的算法，经常在低光增强的算法比较中见到。其核心思想是重建映射函数X=[$x_{0\sim255}$],X由差分变量d来构建。
Graph-Based Representation and Reasoning， 24th International Conference on Conceptual Structures, ICCS 2019, Marburg, Germany, July 1–4, 2019, Proceedings
