Autoencoders have emerged as a useful framework for unsupervised learning of internal
representations, and a wide variety of apparently conceptually disparate regularization …

Recent researches on transfer learning exploit deep structures for discriminative feature
representation to tackle cross-domain disparity. However, few of them are able to joint …

Domain adaptation (DA) aims to generalize a learning model across training and testing
data despite the mismatch of their data distributions. In light of a theoretical estimation of the …

Text classification is considered as one of the primary task in many Natural Language
Processing (NLP) applications. In industrial applications of NLP, sentimental analysis is a …

In leveraging manifold learning in domain adaptation (DA), graph embedding-based DA
methods have shown their effectiveness in preserving data manifold through the Laplace …

Multi-domain learning (MDL) refers to learning a set of models simultaneously, with each
one specialized to perform a task in a certain domain. Generally, high labeling effort is …

Abstract Domain adaptation (DA) aims to enable a learning model trained from a source
domain to generalize well on a target domain, despite the mismatch of data distributions …

During the training process, deep neural networks implicitly learn to represent the input data
samples through a hierarchy of features, where the size of the hierarchy is determined by the …

A predictor, $ f_A: X\to Y $, learned with data from a source domain (A) might not be
accurate on a target domain (B) when their distributions are different. Domain adaptation …

Teaching machines to understand human language is one of the most elusive and long-
standing challenges in Natural Language Processing (NLP). Driven by the fast development …