Deep learning algorithms are a subset of the machine learning algorithms, which aim at
discovering multiple levels of distributed representations. Recently, numerous deep learning …

Hyperspectral image (HSI) classification has become a hot topic in the field of remote
sensing. In general, the complex characteristics of hyperspectral data make the accurate …

Knowledge graph (KG) embedding is to embed components of a KG including entities and
relations into continuous vector spaces, so as to simplify the manipulation while preserving …

An introduction to a broad range of topics in deep learning, covering mathematical and
conceptual background, deep learning techniques used in industry, and research …

Abstract Knowledge graph completion aims to perform link prediction between entities. In
this paper, we consider the approach of knowledge graph embeddings. Recently, models …

Abstract Big Data Analytics and Deep Learning are two high-focus of data science. Big Data
has become important as many organizations both public and private have been collecting …

Continuous space language models have recently demonstrated outstanding results across
a variety of tasks. In this paper, we examine the vector-space word representations that are …

Abstract Knowledge graphs are useful resources for numerous AI applications, but they are
far from completeness. Previous work such as TransE, TransH and TransR/CTransR regard …

Mining valuable hidden knowledge from large-scale data relies on the support of reasoning
technology. Knowledge graphs, as a new type of knowledge representation, have gained …

The success of machine learning algorithms generally depends on data representation, and
we hypothesize that this is because different representations can entangle and hide more or …