Continual learning approaches help deep neural network models adapt and learn
incrementally by trying to solve catastrophic forgetting. However, whether these existing …

Continual Learning (CL) allows applications such as user personalization and household
robots to learn on the fly and adapt to context. This is an important feature when context …

Internet of Things (IoT) systems provide large amounts of data on all aspects of human
behavior. Machine learning techniques, especially deep neural networks (DNN), have …

Deep Learning (DL) has shown impressive performance in many mobile applications. Most
existing works have focused on reducing the computational and resource overheads of …

Various incremental learning (IL) approaches have been proposed to help deep learning
models learn new tasks/classes continuously without forgetting what was learned previously …

Like generic multi-task learning, continual learning has the nature of multi-objective
optimization, and therefore faces a trade-off between the performance of different tasks. That …

Usability challenges and social acceptance of textual input in a context of extended realities
(XR) motivate the research of novel input modalities. We investigate the fusion of inertial …

Algorithms that balance the stability-plasticity trade-off are well-studied in the continual
learning literature. However, only a few of them focus on obtaining models for specified …

2. Background. This chapter describes the relevant research in more details in the areas of
on-device ML and CL to discuss the necessity, novelty, and contributions of this thesis. 3 …

Continual Learning (CL) methods are designed to help deep neural networks to adapt and
learn new tasks/knowledge without forgetting previously learned tasks. In recent years …