Artificial intelligence (AI) research and market have grown rapidly in the last few years, and
this trend is expected to continue with many potential advancements and innovations in this …

In recent years, Deep Neural Networks (DNNs) have emerged as a widely adopted
approach in many application domains. Training DNN models is also becoming a significant …

A fundamental task for artificial intelligence is learning. Deep Neural Networks have proven
to cope perfectly with all learning paradigms, ie supervised, unsupervised, and …

Recently, sparse training methods have started to be established as a de facto approach for
training and inference efficiency in artificial neural networks. Yet, this efficiency is just in …

Artificial intelligence (AI) research and market have grown rapidly in the last few years and
this trend is expected to continue with many potential advancements and innovations in this …

Abstract In Distributed Computation Optimization (DCO) networks, where clients distribute
computational jobs among heterogeneous helpers with different capacities and pricing …

Class-incremental learning (CIL) is a challenging task that involves sequentially learning to
categorize classes from new tasks without forgetting previously learned information. The …

Nowadays, deep learning is a current and stimulating area of machine learning, and it has
proven to remain a successful tool in numerous fields. Nevertheless, the scalability of deep …

Sparsity can be found in the underlying structure of many real-world computationally
expensive problems including big graph analytics and large scale sparse deep neural …

The current disruptive state of High Performance Computing (HPC) and Cloud computing is
made possible by emerging CPU and GPU architectures [18, 95], parallel processing …