Deep neural networks (DNNs) are well-known to be vulnerable to adversarial attacks, where
malicious human-imperceptible perturbations are included in the input to the deep network …

Despite the impressive performance of Artificial Intelligence (AI) systems, their robustness
remains elusive and constitutes a key issue that impedes large-scale adoption. Besides …

The Lipschitz constant of neural networks has been established as a key quantity to enforce
the robustness to adversarial examples. In this paper, we tackle the problem of building $1 …

Grey system theory uses differential equations to model small sample time series to predict
the short-term development law of things in the future. Since the most classical GM (1, 1) …

Although deep neural networks (DNN) have achieved great success, their applications in
safety-critical areas are hindered due to their vulnerability to adversarial attacks. Some …

Diffusion models have achieved notable success in image generation, but they remain
highly vulnerable to backdoor attacks, which compromise their integrity by producing …

Universal approximation capability, also referred to as universality, is an important property
of deep neural networks, endowing them with the potency to accurately represent the …

Despite significant advancements in out-of-distribution (OOD) detection, existing methods
still struggle to maintain robustness against adversarial attacks, compromising their …

The text-to-image generation model has attracted significant interest from both academic
and industrial communities. These models can generate the images based on the given …

Abstract Residual Networks (ResNet) are pivotal in machine learning. The connection
between ResNets and ordinary differential equations (ODEs) has inspired enhancements of …