1 code implementation • 8 Apr 2024 • Tianshuo Cong, Delong Ran, Zesen Liu, Xinlei He, JinYuan Liu, Yichen Gong, Qi Li, Anyu Wang, XiaoYun Wang
Model merging is a promising lightweight model empowerment technique that does not rely on expensive computing devices (e. g., GPUs) or require the collection of specific training data.
1 code implementation • 9 Nov 2023 • Yichen Gong, Delong Ran, JinYuan Liu, Conglei Wang, Tianshuo Cong, Anyu Wang, Sisi Duan, XiaoYun Wang
Ensuring the safety of artificial intelligence-generated content (AIGC) is a longstanding topic in the artificial intelligence (AI) community, and the safety concerns associated with Large Language Models (LLMs) have been widely investigated.
1 code implementation • 27 Jan 2022 • Tianshuo Cong, Xinlei He, Yang Zhang
Recent research has shown that the machine learning model's copyright is threatened by model stealing attacks, which aim to train a surrogate model to mimic the behavior of a given model.
no code implementations • 25 Sep 2019 • Tianshuo Cong, Dan Peng, Furui Liu, Zhitang Chen
Our experiments demonstrate our method is able to correctly identify the bivariate causal relationship between concepts in images and the representation learned enables a do-calculus manipulation to images, which generates artificial images that might possibly break the physical law depending on where we intervene the causal system.