As a promising area in machine learning, multi-task learning (MTL) aims to improve the
performance of multiple related learning tasks by leveraging useful information among them …

Abstract Multi-task learning (MTL), which optimizes multiple related learning tasks at the
same time, has been widely used in various applications, including natural language …

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to
leverage useful information contained in multiple related tasks to help improve the …

Lifelong Machine Learning, Second Edition is an introduction to an advanced machine
learning paradigm that continuously learns by accumulating past knowledge that it then …

In this paper, we focus on heterogeneous feature learning for RGB-D activity recognition.
Considering that features from different channels could share some similar hidden …

Supervised machine learning techniques have already been widely studied and applied to
various real-world applications. However, most existing supervised algorithms work well …

In many practical applications of supervised learning the task involves the prediction of
multiple target variables from a common set of input variables. When the prediction targets …

In many real-world applications we deal with multiple related classification/regression/
clustering tasks. For example, in the prediction of therapy outcome (Bickel et al., 2008), the …

Adaptive networks are suitable for decentralized inference tasks. Recent works have
intensively studied distributed optimization problems in the case where the nodes have to …

Deep convolutional neural networks (CNN) have seen tremendous success in large-scale
generic object recognition. In comparison with generic object recognition, fine-grained …