The research advances concerning the typical architectures of convolutional neural
networks (CNNs) as well as their optimizations are analyzed and elaborated in detail in this …

Recently, a new trend of exploring sparsity for accelerating neural network training has
emerged, embracing the paradigm of training on the edge. This paper proposes a novel …

Deep learning has been used in various domains, including Web services. Convolutional
neural networks (CNNs), which are deep learning representatives, are among the most …

In the context of emergency response applications, real-time situational awareness is vital.
Unmanned aerial vehicles (UAVs) with imagers have emerged as crucial tools for providing …

Recently, the recurrent neural network, or its most popular type—the Long Short Term
Memory (LSTM) network—has achieved great success in a broad spectrum of real-world …

Artificial Neural Networks (ANNs) are known as state-of-the-art techniques in Machine
Learning (ML) and have achieved outstanding results in data-intensive applications, such as …

Recently, sparse training has emerged as a promising paradigm for efficient deep learning
on edge devices. The current research mainly devotes the efforts to reducing training costs …

Tucker decomposition is one of the SOTA CNN model compression techniques. However,
unlike the FLOPs reduction, we observe very limited inference time reduction with Tucker …

Wind turbine wakes are the most significant factor affecting wind farm performance,
decreasing energy production and increasing fatigue loads in downstream turbines. Wind …

The emergence of Neural Architecture Search (NAS) enables an automated neural network
development process that potentially replaces manually-enabled machine learning …