1 code implementation • 31 Mar 2024 • Shaofei Shen, Chenhao Zhang, Yawen Zhao, Alina Bialkowski, Weitong Tony Chen, Miao Xu
Leveraging this approximation, we adapt the original model to eliminate information from the forgotten data at the representation level.
no code implementations • 11 Mar 2024 • Chenhao Zhang, Yongyang Zhou, Lei Zhang
The neural radiance field (NeRF) has emerged as a prominent methodology for synthesizing realistic images of novel views.
1 code implementation • 7 Mar 2024 • Zihan Luo, Xiran Song, Hong Huang, Jianxun Lian, Chenhao Zhang, Jinqi Jiang, Xing Xie
To evaluate and enhance the graph understanding abilities of LLMs, in this paper, we propose a benchmark named GraphInstruct, which comprehensively includes 21 classical graph reasoning tasks, providing diverse graph generation pipelines and detailed reasoning steps.
1 code implementation • 30 Jan 2024 • Shaofei Shen, Chenhao Zhang, Alina Bialkowski, Weitong Chen, Miao Xu
To address this shortcoming, the present study undertakes a causal analysis of the unlearning and introduces a novel framework termed Causal Machine Unlearning (CaMU).
2 code implementations • ICCV 2023 • Lvfang Tao, Wei Gao, Ge Li, Chenhao Zhang
Compressive autoencoders (CAEs) play an important role in deep learning-based image compression, but large-scale CAEs are computationally expensive.
no code implementations • 19 May 2022 • Yawen Zhao, Mingzhe Zhang, Chenhao Zhang, Weitong Chen, Nan Ye, Miao Xu
This is because AdaPU learns a weak classifier and its weight using a weighted positive-negative (PN) dataset with some negative data weights $-$ the dataset is derived from the original PU data, and the data weights are determined by the current weighted classifier combination, but some data weights are negative.