no code implementations • 8 Jan 2024 • Zhongjiang He, Zihan Wang, Xinzhang Liu, Shixuan Liu, Yitong Yao, Yuyao Huang, Xuelong Li, Yongxiang Li, Zhonghao Che, Zhaoxi Zhang, Yan Wang, Xin Wang, Luwen Pu, Huinan Xu, Ruiyu Fang, Yu Zhao, Jie Zhang, Xiaomeng Huang, Zhilong Lu, Jiaxin Peng, Wenjun Zheng, Shiquan Wang, Bingkai Yang, Xuewei he, Zhuoru Jiang, Qiyi Xie, Yanhan Zhang, Zhongqiu Li, Lingling Shi, Weiwei Fu, Yin Zhang, Zilu Huang, Sishi Xiong, Yuxiang Zhang, Chao Wang, Shuangyong Song
Subsequently, the model undergoes fine-tuning to align with human preferences, following a detailed methodology that we describe.
1 code implementation • 18 Apr 2023 • Xiaomei Zhang, Zhaoxi Zhang, Qi Zhong, Xufei Zheng, Yanjun Zhang, Shengshan Hu, Leo Yu Zhang
To explore how to use the masked language model in adversarial detection, we propose a novel textual adversarial example detection method, namely Masked Language Model-based Detection (MLMD), which can produce clearly distinguishable signals between normal examples and adversarial examples by exploring the changes in manifolds induced by the masked language model.
1 code implementation • 14 May 2022 • Zhaoxi Zhang, Leo Yu Zhang, Xufei Zheng, Bilal Hussain Abbasi, Shengshan Hu
The usage of deep learning is being escalated in many applications.
no code implementations • NeurIPS 2021 • Zhaoxi Zhang, Leo Yu Zhang, Xufei Zheng, Jinyu Tian, Jiantao Zhou
To alleviate this problem, we explore how to detect adversarial examples with disentangled label/semantic features under the autoencoder structure.
no code implementations • 7 Dec 2018 • Zecang Gu, Yin Liang, Zhaoxi Zhang
In this paper we proposed an ultimate theory to solve the multi-target control problem through its introduction to the machine learning framework in automatic driving, which explored the implementation of excellent drivers' knowledge acquisition.