DNNs are becoming less and less over-parametrised due to recent advances in efficient
model design, through careful hand-crafted or NAS-based methods. Relying on the fact that …

Deep neural networks are generally designed as a stack of differentiable layers, in which a
prediction is obtained only after running the full stack. Recently, some contributions have …

Artificial intelligence (AI) technologies have emerged as pivotal enablers across a multitude
of industries, including consumer electronics, healthcare, and manufacturing, largely due to …

Deep neural networks are state of the art methods for many learning tasks due to their ability
to extract increasingly better features at each network layer. However, the improved …

Spiking Neural Networks (SNNs) have recently emerged as an alternative to deep learning
owing to sparse, asynchronous and binary event (or spike) driven processing, that can yield …

The ubiquitous usage of wearable IoT (wIoT) devices has created a formidable opportunity
for remote health monitoring system to provide paramount services such as preventive care …

Facial Expression Recognition (FER) in the wild poses significant challenges due to realistic
occlusions, illumination, scale, and head pose variations of the facial images. In this article …

Early exiting is an effective paradigm for improving the inference efficiency of deep networks.
By constructing classifiers with varying resource demands (the exits), such networks allow …

Modern convolutional neural networks apply the same operations on every pixel in an
image. However, not all image regions are equally important. To address this inefficiency …

Abstract Spiking Neural Networks (SNNs) have recently become more popular as a
biologically plausible substitute for traditional Artificial Neural Networks (ANNs). SNNs are …