Autoencoders have become a hot researched topic in unsupervised learning due to their
ability to learn data features and act as a dimensionality reduction method. With rapid …

Text clustering is a task for grouping extracted information of the text in different clusters,
which has many applications in recommender systems, sentiment analysis, and more. Deep …

Background: Autonomous ships have the potential to increase operational efficiency and
reduce carbon footprints through technology and innovation. However, there is no …

Document clustering involves grouping together documents so that similar documents are
grouped together in the same cluster and different documents in the different clusters …

Abstract The construction of Smart Cities is inseparable from the healthy operation of
markets. Reasonable data analysis can provide a crucial foundation for the development of …

Text clustering is a method for separating specific information from textual data and can
even classify text according to topic and sentiment, which has drawn much interest in recent …

In this paper, a new theory to train neural networks is presented which is called “Neural
Network Meaningful Learning”(NNMeL) theory. According to this theory, meaningful learning …

Language models have been shown to reproduce underlying biases existing in their training
data, which is the majority perspective by default. Proposed solutions aim to capture minority …

Abstract Model optimization in deep learning (DL) and neural networks is concerned about
how and why the model can be successfully trained towards one or more objective …

针对原始深度嵌入聚类(DEC) 算法中聚类层得出的初始聚类数目和聚类中心有很强的随机性,
从而影响DEC 算法效果的问题, 提出一种基于改进DEC 的评论文本聚类算法 …