Large Language Models (LLMs) went from non-existent to ubiquitous in the machine
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …

Recent successes in deep learning for vision and natural language processing are
attributed to larger models but come with energy consumption and scalability issues. Current …

The amalgamation of artificial intelligence with Internet of Things (AIoT) devices have seen a
rapid surge in growth, largely due to the effective implementation of deep neural network …

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 …

Forward Gradients-the idea of using directional derivatives in forward differentiation mode-
have recently been shown to be utilizable for neural network training while avoiding …

Deep neural networks conventionally employ end-to-end backpropagation for their training
process, which lacks biological credibility and triggers a locking dilemma during network …

Distributed deep learning frameworks like federated learning (FL) and its variants are
enabling personalized experiences across a wide range of web clients and mobile/IoT …

Deep neural networks are typically trained using global error signals that backpropagate
(BP) end-to-end, which is not only biologically implausible but also suffers from the update …

With the rapid growth in the volume of data sets, models, and devices in the domain of deep
learning, there is increasing attention on large-scale distributed deep learning. In contrast to …

Abstract End-to-end (E2E) training has become the de-facto standard for training modern
deep networks, eg, ConvNets and vision Transformers (ViTs). Typically, a global error signal …