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 …

Modern federated networks, such as those comprised of wearable devices, mobile phones,
or autonomous vehicles, generate massive amounts of data each day. This wealth of data …

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 …

Deep networks trained on large-scale data can learn transferable features to promote
learning multiple tasks. Since deep features eventually transition from general to specific …

This paper proposes a joint multi-task learning algorithm to better predict attributes in
images using deep convolutional neural networks (CNN). We consider learning binary …

In the paradigm of multi-task learning, mul-tiple related prediction tasks are learned jointly,
sharing information across the tasks. We propose a framework for multi-task learn-ing that …

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

Learning-to-learn or meta-learning leverages data-driven inductive bias to increase the
efficiency of learning on a novel task. This approach encounters difficulty when transfer is …

Deep neural networks trained on large-scale dataset can learn transferable features that
promote learning multiple tasks for inductive transfer and labeling mitigation. As deep …

Multitask learning is a learning paradigm that seeks to improve the generalization
performance of a learning task with the help of some other related tasks. In this article, we …