Zobrazeno 1 - 10
of 2 256
pro vyhledávání: '"Liu Sijia"'
Autor:
Deng Dejun, Huang Yanghua, You Shufeng, Li Shengcai, Liu Yanmin, Chi Changjia, Huang Siqi, Liu Sijia
Publikováno v:
E3S Web of Conferences, Vol 490, p 02005 (2024)
Urban design rainstorm is the basis of urban hydraulic engineering design, which is related to the safety and economy of urban drainage engineering, and has an important impact on the design scale and investment of urban drainage engineering. Scienti
Externí odkaz:
https://doaj.org/article/a22701dae7c4497499b08c26115746a5
Autor:
Zhang, Yimeng, Zhi, Tiancheng, Liu, Jing, Sang, Shen, Jiang, Liming, Yan, Qing, Liu, Sijia, Luo, Linjie
The ability to synthesize personalized group photos and specify the positions of each identity offers immense creative potential. While such imagery can be visually appealing, it presents significant challenges for existing technologies. A persistent
Externí odkaz:
http://arxiv.org/abs/2411.13632
Autor:
Zhang, Ruichen, Yao, Yuguang, Tan, Zhen, Li, Zhiming, Wang, Pan, Liu, Huan, Hu, Jingtong, Liu, Sijia, Chen, Tianlong
Image generation is a prevailing technique for clinical data augmentation for advancing diagnostic accuracy and reducing healthcare disparities. Diffusion Model (DM) has become a leading method in generating synthetic medical images, but it suffers f
Externí odkaz:
http://arxiv.org/abs/2410.22551
The need for effective unlearning mechanisms in large language models (LLMs) is increasingly urgent, driven by the necessity to adhere to data regulations and foster ethical generative AI practices. Despite growing interest of LLM unlearning, much of
Externí odkaz:
http://arxiv.org/abs/2410.17509
In this work, we address the problem of large language model (LLM) unlearning, aiming to remove unwanted data influences and associated model capabilities (e.g., copyrighted data or harmful content generation) while preserving essential model utiliti
Externí odkaz:
http://arxiv.org/abs/2410.07163
Autor:
Ferraz, Thomas Palmeira, Mehta, Kartik, Lin, Yu-Hsiang, Chang, Haw-Shiuan, Oraby, Shereen, Liu, Sijia, Subramanian, Vivek, Chung, Tagyoung, Bansal, Mohit, Peng, Nanyun
Instruction following is a key capability for LLMs. However, recent studies have shown that LLMs often struggle with instructions containing multiple constraints (e.g. a request to create a social media post "in a funny tone" with "no hashtag"). Desp
Externí odkaz:
http://arxiv.org/abs/2410.06458
Despite the remarkable generation capabilities of Diffusion Models (DMs), conducting training and inference remains computationally expensive. Previous works have been devoted to accelerating diffusion sampling, but achieving data-efficient diffusion
Externí odkaz:
http://arxiv.org/abs/2409.19128
Object detection is a crucial task in autonomous driving. While existing research has proposed various attacks on object detection, such as those using adversarial patches or stickers, the exploration of projection attacks on 3D surfaces remains larg
Externí odkaz:
http://arxiv.org/abs/2409.17403
Watermarking is an essential technique for embedding an identifier (i.e., watermark message) within digital images to assert ownership and monitor unauthorized alterations. In face recognition systems, watermarking plays a pivotal role in ensuring da
Externí odkaz:
http://arxiv.org/abs/2409.16056
Autor:
Chen, Yuyan, Yu, Tianhao, Li, Yueze, Yan, Songzhou, Liu, Sijia, Liang, Jiaqing, Xiao, Yanghua
The evaluation of the problem-solving capability under incomplete information scenarios of Large Language Models (LLMs) is increasingly important, encompassing capabilities such as questioning, knowledge search, error detection, and path planning. Cu
Externí odkaz:
http://arxiv.org/abs/2409.14762