Unstructured pruning reduces the memory footprint in deep neural networks (DNNs).
Recently, researchers proposed different types of structural pruning intending to reduce also …

Structured channel pruning has been shown to significantly accelerate inference time for
convolution neural networks (CNNs) on modern hardware, with a relatively minor loss of …

Recently issued data privacy regulations like GDPR (General Data Protection Regulation)
grant individuals the right to be forgotten. In the context of machine learning, this requires a …

In deep learning, fine-grained N: M sparsity reduces the data footprint and bandwidth of a
General Matrix multiply (GEMM) by x2, and doubles throughput by skipping computation of …

Today, creators of data-hungry deep neural networks (DNNs) scour the Internet for training
fodder, leaving users with little control over or knowledge of when their data is appropriated …

Deep learning intellectual properties (IPs) are high-value assets that are frequently
susceptible to theft. This vulnerability has led to significant interest in defending the field's …

In deep learning, fine-grained N: M sparsity reduces the data footprint and bandwidth of a
General Matrix multiply (GEMM) up to x2, and doubles throughput by skipping computation …

As machine learning (ML) models have grown in size and scope in recent years, so has the
amount of data needed to train them. Unfortunately, individuals whose data is used in large …

Anomaly detection is an important task for complex systems (eg, industrial facilities,
manufacturing, large-scale science experiments), where failures in a sub-system can lead to …

During the last decade, machine learning (ML) has achieved tremendous results in many
fields, from traditional learning tasks like image recognition to advanced applications such …