Deep neural networks (DNNs) provide excellent performance across a wide range of
classification tasks, but their training requires high computational resources and is often …

Weight pruning methods for deep neural networks (DNNs) have been investigated recently,
but prior work in this area is mainly heuristic, iterative pruning, thereby lacking guarantees …

Deep neural networks enable state-of-the-art accuracy on visual recognition tasks such as
image classification and object detection. However, modern deep networks contain millions …

With the increasing stringent QoS constraints (eg, latency, bandwidth, jitter) imposed by
novel applications (eg, e-Health, autonomous vehicles, smart cities, etc.), as well as the …

Over the last decade, machine learning (ML) and deep learning (DL) algorithms have
significantly evolved and been employed in diverse applications, such as computer vision …

Deep neural network compression has the potential to bring modern resource-hungry deep
networks to resource-limited devices. However, in many of the most compelling deployment …

With the general trend of increasing Convolutional Neural Network (CNN) model sizes,
model compression and acceleration techniques have become critical for the deployment of …

We present an efficient finetuning methodology for neural-network filters which are applied
as a postprocessing artifact-removal step in video coding pipelines. The fine-tuning is …

Deep learning technologies have demonstrated remarkable effectiveness in a wide range of
tasks, and deep learning holds the potential to advance a multitude of applications …

Network pruning is a widely used technique to reduce computation cost and model size for
deep neural networks. However, the typical three-stage pipeline, ie, training, pruning and …