Zobrazeno 1 - 10
of 87
pro vyhledávání: '"Zhou, Shiji"'
Unlearning Concepts in Diffusion Model via Concept Domain Correction and Concept Preserving Gradient
Autor:
Wu, Yongliang, Zhou, Shiji, Yang, Mingzhuo, Wang, Lianzhe, Zhu, Wenbo, Chang, Heng, Zhou, Xiao, Yang, Xu
Current text-to-image diffusion models have achieved groundbreaking results in image generation tasks. However, the unavoidable inclusion of sensitive information during pre-training introduces significant risks such as copyright infringement and pri
Externí odkaz:
http://arxiv.org/abs/2405.15304
Concept Bottleneck Models (CBMs) map the black-box visual representations extracted by deep neural networks onto a set of interpretable concepts and use the concepts to make predictions, enhancing the transparency of the decision-making process. Mult
Externí odkaz:
http://arxiv.org/abs/2404.08978
Autor:
He, Yifei, Zhou, Shiji, Zhang, Guojun, Yun, Hyokun, Xu, Yi, Zeng, Belinda, Chilimbi, Trishul, Zhao, Han
Multi-task learning (MTL) considers learning a joint model for multiple tasks by optimizing a convex combination of all task losses. To solve the optimization problem, existing methods use an adaptive weight updating scheme, where task weights are dy
Externí odkaz:
http://arxiv.org/abs/2402.02009
Autor:
Ma, Rui, Zhou, Qiang, Jin, Yizhu, Zhou, Daquan, Xiao, Bangjun, Li, Xiuyu, Qu, Yi, Singh, Aishani, Keutzer, Kurt, Hu, Jingtong, Xie, Xiaodong, Dong, Zhen, Zhang, Shanghang, Zhou, Shiji
Copyright law confers upon creators the exclusive rights to reproduce, distribute, and monetize their creative works. However, recent progress in text-to-image generation has introduced formidable challenges to copyright enforcement. These technologi
Externí odkaz:
http://arxiv.org/abs/2403.12052
Autor:
Zhang, Zhi, Zhang, Qizhe, Gao, Zijun, Zhang, Renrui, Shutova, Ekaterina, Zhou, Shiji, Zhang, Shanghang
Publikováno v:
CVPR2024
With the growing size of pre-trained models, full fine-tuning and storing all the parameters for various downstream tasks is costly and infeasible. In this paper, we propose a new parameter-efficient fine-tuning method, Gradient-based Parameter Selec
Externí odkaz:
http://arxiv.org/abs/2312.10136
Autor:
Cai, Tianchi, Jiang, Jiyan, Zhang, Wenpeng, Zhou, Shiji, Song, Xierui, Yu, Li, Gu, Lihong, Zeng, Xiaodong, Gu, Jinjie, Zhang, Guannan
We study the budget allocation problem in online marketing campaigns that utilize previously collected offline data. We first discuss the long-term effect of optimizing marketing budget allocation decisions in the offline setting. To overcome the cha
Externí odkaz:
http://arxiv.org/abs/2309.02669
Autor:
Cai, Tianchi, Bao, Shenliao, Jiang, Jiyan, Zhou, Shiji, Zhang, Wenpeng, Gu, Lihong, Gu, Jinjie, Zhang, Guannan
Model-free RL-based recommender systems have recently received increasing research attention due to their capability to handle partial feedback and long-term rewards. However, most existing research has ignored a critical feature in recommender syste
Externí odkaz:
http://arxiv.org/abs/2308.13246
Current learning-based edge caching schemes usually suffer from dynamic content popularity, e.g., in the emerging short video platforms, users' request patterns shift significantly over time and across different edges. An intuitive solution for a spe
Externí odkaz:
http://arxiv.org/abs/2308.04205
Autor:
Wang, Xin, Chang, Heng, Xie, Beini, Bian, Tian, Zhou, Shiji, Wang, Daixin, Zhang, Zhiqiang, Zhu, Wenwu
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering 2023 (IEEE TKDE 2023)
Graph neural networks (GNNs) have achieved tremendous success in the task of graph classification and its diverse downstream real-world applications. Despite the huge success in learning graph representations, current GNN models have demonstrated the
Externí odkaz:
http://arxiv.org/abs/2208.06651
The phenomenon of data distribution evolving over time has been observed in a range of applications, calling the needs of adaptive learning algorithms. We thus study the problem of supervised gradual domain adaptation, where labeled data from shiftin
Externí odkaz:
http://arxiv.org/abs/2204.11644