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

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 …

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 …

Real-world environments are inherently non-stationary, frequently introducing new classes
over time. This is especially common in time series classification, such as the emergence of …

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

Many deep learning applications, like keyword spotting [1],[2], require the incorporation of
new concepts (classes) over time, referred to as Class Incremental Learning (CIL). The …

The deep neural network shows excellent performance on a single task. However, deep
neural networks performance degraded when trained continuously on a sequence of new …

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 …

Audio analysis is useful in many application scenarios. The state-of-the-art audio analysis
approaches assume that the data distribution at training and deployment time will be the …

This paper addresses the design of an area and energy efficient hardware accelerator that
supports on-the-fly pruning in neural networks. In a layer of N neurons, the accelerator …