Vision transformers have recently demonstrated great success in various computer vision
tasks, motivating a tremendously increased interest in their deployment into many real-world …

To defend against various adversarial attacks, it is essential to develop a robust and high
computing efficiency defence framework. Adversarial ensemble learning is the most effective …

While diffusion models have demonstrated exceptional image generation capabilities, the
iterative noise estimation process required for these models is compute-intensive and their …

Deep learning (DL) models are enabling a significant paradigm shift in a diverse range of
fields, including natural language processing and computer vision, as well as the design …

Low-precision quantization and sparsity have been widely explored in CNN acceleration
due to their effectiveness in reducing computational complexity and memory requirements …

DNNs are known to be vulnerable to so-called adversarial attacks that manipulate inputs to
cause incorrect results that can be beneficial to an attacker or damaging to the victim …

Transfer learning leverages feature representations of deep neural networks (DNNs)
pretrained on source tasks with rich data to empower effective finetuning on downstream …

Understanding how to defend against adversarial attacks is crucial for ensuring the safety
and reliability of these systems in real-world applications. Various adversarial defense …

Recent research has shown that large language models (LLMs) can utilize low-precision
floating point (FP) quantization to deliver high efficiency while maintaining original model …

Machine learning (ML) has seen a major rise in popularity on edge devices in recent years,
ranging from IoT devices to self-driving cars. Security in a critical consideration on these …