Federated Learning (FL) is a collaboratively decentralized privacy-preserving technology to
overcome challenges of data silos and data sensibility. Exactly what research is carrying the …

Transfer learning aims at improving the performance of target learners on target domains by
transferring the knowledge contained in different but related source domains. In this way, the …

近年来, 迁移学习已经引起了广泛的关注和研究. 迁移学习是运用已存有的知识对不同但相关
领域问题进行求解的一种新的机器学习方法. 它放宽了传统机器学习中的两个基本假设:(1) …

Recently, sequential transfer learning emerged as a modern technique for applying the
“pretrain then fine-tune” paradigm to leverage existing knowledge to improve the …

Machine learning relies on the availability of vast amounts of data for training. However, in
reality, data are mostly scattered across different organizations and cannot be easily …

Domain adaptation has become a prominent problem setting in machine learning and
related fields. This review asks the question: How can a classifier learn from a source …

Deep neural networks excel at learning from labeled data and achieve state-of-the-art
resultson a wide array of Natural Language Processing tasks. In contrast, learning from …

Sentiments or opinions from social media provide the most up-to-date and inclusive
information, due to the proliferation of social media and the low barrier for posting the …

Language is often regarded as the hallmark of human intelligence. Developing systems that
can understand human language is thus one of the main obstacles on the quest towards …

Affective computing is an emerging interdisciplinary research field bringing together
researchers and practitioners from various fields, ranging from artificial intelligence, natural …