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用于词嵌入的词级最大均值差异正则化AI 解读

The technique of word embedding is widely used in natural language processing (NLP) to represent words as numerical vectors in textual datasets. However, the estimation of word embedding may suffer from severe overfitting due to the huge variety of words. To address the issue, this article proposes a novel regularization framework that recognizes and accounts for the “word-level distribution discrepancy”–a common phenomenon in a range of NLP tasks where word distributions…

耦合生成：结合直接与间接生成器的实例生成方法AI 解读

Instance generation creates representative examples to interpret a learning model, as in regression and classification. For example, representative sentences of a topic of interest describe the topic specifically for sentence categorization. In such a situation, a large number of unlabeled observations may be available in addition to labeled data, for example, many unclassified text corpora (unlabeled instances) are available with only a few classified sentences (labeled…

嵌入学习AI 解读

Numerical embedding has become one standard technique for processing and analyzing unstructured data that cannot be expressed in a predefined fashion. It stores the main characteristics of data by mapping it onto a numerical vector. An embedding is often unsupervised and constructed by transfer learning from large-scale unannotated data. Given an embedding, a downstream learning method, referred to as a two-stage method, is applicable to unstructured data. In this article,…

面向个性化预测的可扩展协同排序AI 解读

Ben Daiab, Xiaotong Shena, Junhui Wangb & Annie Quc* a School of Statistics, University of Minnesota , Minneapolis , MN ; b School of Data Science, City University of Hong Kong , Kowloon Tong, Hong Kong ; c Department of Statistics, University of Illinois at Urbana Champaign , Champaign , IL Supplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA. CONTACT Annie Qu anniequ@illinois.edu Department of Statistics, University of…