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 …

Abstract The Internet of Things (IoT) has transformed many aspects of modern life, from
healthcare and transportation to home automation and industrial control systems. However …

Reinforcement learning is a powerful framework for robots to acquire skills from experience,
but often requires a substantial amount of online data collection. As a result, it is difficult to …

While huge volumes of unlabeled data are generated and made available in many domains,
the demand for automated understanding of visual data is higher than ever before. Most …

Visual model-based RL methods typically encode image observations into low-dimensional
representations in a manner that does not eliminate redundant information. This leaves them …

In lifelong machine learning (LML) systems, consecutive new tasks from changing
circumstances are learned and added to the system. However, sufficiently labeled data are …

Accurately predicting the urban spatio-temporal data is critically important to various urban
computing tasks for smart city related applications such as crowd flow prediction and traffic …

Recently, convolutional neural network (CNN) has been widely investigated to decode
human intentions using surface Electromyography (sEMG) signals. However, a pre-trained …

Adapting to a constantly changing environment requires the human brain to flexibly switch
among many demanding cognitive tasks, processing both specialized and integrated …

Leakage in water distribution systems is a significant problem worldwide, leading to wastage
of water resources, compromised water quality and excess energy consumption. Leakage …