Machine learning (ML) is widely used today, especially through deep neur...
Transformers' compute-intensive operations pose enormous challenges for ...
Capsule Networks (CapsNets) are able to hierarchically preserve the pose...
Adversarial training is exploited to develop a robust Deep Neural Networ...
Neural Architecture Search (NAS) algorithms aim at finding efficient Dee...
Autonomous Driving (AD) related features represent important elements fo...
In today's era of smart cyber-physical systems, Deep Neural Networks (DN...
Complex Deep Neural Networks such as Capsule Networks (CapsNets) exhibit...
Recently, Deep Neural Networks (DNNs) have achieved remarkable performan...
Spiking Neural Networks (SNNs) aim at providing energy-efficient learnin...
Spiking Neural Networks (SNNs), despite being energy-efficient when
impl...
Autonomous Driving (AD) related features provide new forms of mobility t...
Currently, Machine Learning (ML) is becoming ubiquitous in everyday life...
Deep Neural Networks (DNNs) have made significant improvements to reach ...
Spiking Neural Networks (SNNs), the third generation NNs, have come unde...
Due to their proven efficiency, machine-learning systems are deployed in...
Capsule Networks (CapsNets), recently proposed by the Google Brain team,...
Convolutional Neural Networks (CNNs) are extensively in use due to their...
Recently, many adversarial examples have emerged for Deep Neural Network...
Capsule Networks envision an innovative point of view about the
represen...
Activation functions influence behavior and performance of DNNs. Nonline...