The time required for training the neural networks increases with size, complexity, and
depth. Training model parameters by backpropagation inherently creates feedback loops …

Deep learning services based on cloud computing have deficiencies in latency, privacy, etc.
To meet the requirements of low latency, researchers have begun to consider the …

Deep neural networks are gaining importance and popularity in applications and services.
Due to the enormous number of learnable parameters and datasets, the training of neural …

Due to industrial demands to handle increasing amounts of training data, lower the cost of
computing one model at a time, and lessen the ecological effects of intensive computing …

Artificial intelligence (AI), especially deep learning, requires vast amounts of data for
training, testing, and validation. Collecting these data and the corresponding annotations …

Sarcastic remarks often appear in social media and e-commerce platforms to express almost
exclusively negative emotions and opinions on certain instances, such as dissatisfaction …

The goal of this work is to evaluate a deep learning algorithm that has been designed to
predict the topological evolution of dynamic complex non-Euclidean graphs in discrete–time …

Coping with the growing demand for data and parameters in complex neural network (NN)
models of contemporary times typically involves distributing them across multiple devices …

Many of the machine learning tasks focus on cen-tralized learning (CL), which requires the
transmission of localdatasets from the clients to a parameter server (PS) entailing …

With the popularity of Deep Neural Network (DNN) models in diverse fields, DNN inference
services have been widely deployed on cloud for resource-limited devices to support …