Image classification has always been a hot research direction in the world, and the
emergence of deep learning has promoted the development of this field. Convolutional …

Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

Deep neural networks have been applied in many applications exhibiting extraordinary
abilities in the field of computer vision. However, complex network architectures challenge …

Cross-silo federated learning (FL) enables organizations (eg, financial, or medical) to
collaboratively train a machine learning model by aggregating local gradient updates from …

We present the next generation of MobileNets based on a combination of complementary
search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile …

In recent years, machine learning (ML) and deep learning (DL) have shown remarkable
improvement in computer vision, natural language processing, stock prediction, forecasting …

Edge intelligence refers to a set of connected systems and devices for data collection,
caching, processing, and analysis proximity to where data are captured based on artificial …

In this paper, we propose several ideas for enhancing a binary network to close its accuracy
gap from real-valued networks without incurring any additional computational cost. We first …

Spiking neural networks (SNNs) are inspired by information processing in biology, where
sparse and asynchronous binary signals are communicated and processed in a massively …

Convolutional neural networks require significant memory bandwidth and storage for
intermediate computations, apart from substantial computing resources. Neural network …