Deep Neural Networks (DNNs), from AlexNet to ResNet to ChatGPT, have made
revolutionary progress in recent years, and are widely used in various fields. The high …

Productionizing machine learning projects is inherently complex, involving a multitude of
interconnected components that are assembled like LEGO blocks and evolve throughout …

Deep learning models and the digital records they generate have remarkably increased
their adoption of many practical applications. While the success of deep learning in …

Recent advances in artificial intelligence offer groundbreaking alternatives to conventional
codebook-based channel state information (CSI) feedback techniques. Confronted with the …

Diffusion models have demonstrated remarkable capabilities across a range of tasks and
have become the backbone of various web applications, such as text-to-image, image-to …

Due to enormous computing and storage overhead for well-trained Deep Neural Network
(DNN) models, protecting the intellectual property of model owners is a pressing need. As …

We consider the following problem: given the weights of two models, can we test whether
they were trained independently--ie, from independent random initializations? We consider …

With the growing applications of Deep Learning (DL), especially recent spectacular
achievements of Large Language Models (LLMs) such as ChatGPT and LLaMA, the …

In the rapidly advancing field of deep learning, a significant yet often overlooked challenge
is the protection of intellectual property (IP) for models based on electroencephalography …

Model reuse techniques can reduce the resource requirements for training high-
performance deep neural networks (DNNs) by leveraging existing models. However …