6 accepted papers
In the pre-training-fine-tuning paradigm, pre-trained encoders have become high-value intellectual property (IP) due to their immense training costs, necessitating robust protection. Existing fingerprinting or watermarking methods typically rely on pre-defined samples and labels, or require intrusiv…
Neural Architecture Search (NAS) automates the design of Vision Transformer (ViT) architectures. However, the high computational cost of training-based methods has made training-free, zero-cost proxies a key research direction. While existing proxies can estimate model potential, they fail to captur
Machine Unlearning (MU) has emerged as a promising solution for removing the influence of data that an owner wishes to unlearn from Large Language Models (LLMs). However, existing MU methods, which require tuning the entire model parameters on the unlearned data with random labels or perturbed gradi…
Retrieval-Augmented Generation (RAG) is a state-of-the-art technique that mitigates issues such as hallucinations and knowledge staleness in Large Language Models (LLMs) by retrieving relevant knowledge from an external database to assist in content generation. Existing research has demonstrated pot…
In recent years, deep neural networks (DNNs) have witnessed extensive applications, and protecting their intellectual property (IP) is thus crucial. As a non-invasive way for model IP protection, model fingerprinting has become popular. However, existing single-point based fingerprinting methods are…
The practical needs of the "right to be forgotten" and poisoned data removal call for efficient machine unlearning techniques, which enable machine learning models to unlearn, or to forget a fraction of training data and its lineage. Recent studies on machine unlearning for deep neural networks (DNN…