Zobrazeno 1 - 10
of 889
pro vyhledávání: '"Xu, Jianliang"'
Autor:
Liang, Zi, Ye, Qingqing, Wang, Yanyun, Zhang, Sen, Xiao, Yaxin, Li, Ronghua, Xu, Jianliang, Hu, Haibo
Model extraction attacks (MEAs) on large language models (LLMs) have received increasing research attention lately. Existing attack methods on LLMs inherit the extraction strategies from those designed for deep neural networks (DNNs) yet neglect the
Externí odkaz:
http://arxiv.org/abs/2409.02718
Machine unlearning as an emerging research topic for data regulations, aims to adjust a trained model to approximate a retrained one that excludes a portion of training data. Previous studies showed that class-wise unlearning is successful in forgett
Externí odkaz:
http://arxiv.org/abs/2406.08288
Graph analysis is fundamental in real-world applications. Traditional approaches rely on SPARQL-like languages or clicking-and-dragging interfaces to interact with graph data. However, these methods either require users to possess high programming sk
Externí odkaz:
http://arxiv.org/abs/2401.12672
Due to the unstructuredness and the lack of schemas of graphs, such as knowledge graphs, social networks, and RDF graphs, keyword search for querying such graphs has been proposed. As graphs have become voluminous, large-scale distributed processing
Externí odkaz:
http://arxiv.org/abs/2309.01199
We study the election control problem with multi-votes, where each voter can present a single vote according different views (or layers, we use "layer" to represent "view"). For example, according to the attributes of candidates, such as: education,
Externí odkaz:
http://arxiv.org/abs/2306.17430
Publikováno v:
22nd USENIX Conference on File and Storage Technologies (FAST' 24), 2024
Blockchain systems suffer from high storage costs as every node needs to store and maintain the entire blockchain data. After investigating Ethereum's storage, we find that the storage cost mostly comes from the index, i.e., Merkle Patricia Trie (MPT
Externí odkaz:
http://arxiv.org/abs/2306.10739
Out-of-distribution (OOD) detection is an indispensable aspect of secure AI when deploying machine learning models in real-world applications. Previous paradigms either explore better scoring functions or utilize the knowledge of outliers to equip th
Externí odkaz:
http://arxiv.org/abs/2306.03715
Autor:
Amer-Yahia, Sihem, Bonifati, Angela, Chen, Lei, Li, Guoliang, Shim, Kyuseok, Xu, Jianliang, Yang, Xiaochun
This discussion was conducted at a recent panel at the 28th International Conference on Database Systems for Advanced Applications (DASFAA 2023), held April 17-20, 2023 in Tianjin, China. The title of the panel was "What does LLM (ChatGPT) Bring to D
Externí odkaz:
http://arxiv.org/abs/2306.01388
Privacy and security concerns in real-world applications have led to the development of adversarially robust federated models. However, the straightforward combination between adversarial training and federated learning in one framework can lead to t
Externí odkaz:
http://arxiv.org/abs/2303.00250
Autor:
Guo, Yike, Liu, Qifeng, Chen, Jie, Xue, Wei, Fu, Jie, Jensen, Henrik, Rosas, Fernando, Shaw, Jeffrey, Wu, Xing, Zhang, Jiji, Xu, Jianliang
This report presents a comprehensive view of our vision on the development path of the human-machine symbiotic art creation. We propose a classification of the creative system with a hierarchy of 5 classes, showing the pathway of creativity evolving
Externí odkaz:
http://arxiv.org/abs/2209.02388