no code implementations • 24 Mar 2024 • Lijin Wu, Shanshan Lei, Feilong Liao, Yuanjun Zheng, Yuxin Liu, Wentao Fu, Hao Song, Jiajun Zhou
As the number of IoT devices increases, security concerns become more prominent.
no code implementations • 31 Jan 2024 • Benoit Baudry, Khashayar Etemadi, Sen Fang, Yogya Gamage, Yi Liu, Yuxin Liu, Martin Monperrus, Javier Ron, André Silva, Deepika Tiwari
The results show that LLMs can successfully generate realistic test data generators in a wide range of domains at all three levels of integrability.
no code implementations • 30 Jan 2024 • Thomas Degris, Khurram Javed, Arsalan SharifNassab, Yuxin Liu, Richard Sutton
We conclude by suggesting that combining both approaches could be a promising future direction to improve the performance of neural networks in continual learning.
no code implementations • 21 Nov 2023 • Yuxin Liu, Minshan Xie, Hanyuan Liu, Tien-Tsin Wong
In this paper, we propose a synchronized multi-view diffusion approach that allows the diffusion processes from different views to reach a consensus of the generated content early in the process, and hence ensures the texture consistency.
no code implementations • 4 Oct 2023 • Zhihao Zong, Fazhi He, Rubin Fan, Yuxin Liu
Computer Aided Design (CAD), especially the feature-based parametric CAD, plays an important role in modern industry and society.
no code implementations • 1 Jun 2023 • Jinbo Xing, Menghan Xia, Yuxin Liu, Yuechen Zhang, Yong Zhang, Yingqing He, Hanyuan Liu, Haoxin Chen, Xiaodong Cun, Xintao Wang, Ying Shan, Tien-Tsin Wong
Our method, dubbed Make-Your-Video, involves joint-conditional video generation using a Latent Diffusion Model that is pre-trained for still image synthesis and then promoted for video generation with the introduction of temporal modules.
1 code implementation • 17 Oct 2022 • Yongchang Hao, Yuxin Liu, Lili Mou
We additionally propose a simple modification to stabilize the RL training on non-parallel datasets with our induced reward function.
no code implementations • 12 Oct 2022 • Yuxin Liu, Yawen Li, Yingxia Shao, Zeli Guan
Therefore, a hypergraph neural network model based on dual channel convolution is proposed.
no code implementations • 30 Mar 2021 • Weiqing Min, Zhiling Wang, Yuxin Liu, Mengjiang Luo, Liping Kang, Xiaoming Wei, Xiaolin Wei, Shuqiang Jiang
Food2K can be further explored to benefit more food-relevant tasks including emerging and more complex ones (e. g., nutritional understanding of food), and the trained models on Food2K can be expected as backbones to improve the performance of more food-relevant tasks.
1 code implementation • 10 Oct 2020 • Qiansheng Wang, Yuxin Liu, Chengguo Lv, Zhen Wang, Guohong Fu
Open-domain response generation is the task of generating sensible and informative re-sponses to the source sentence.
no code implementations • 13 Mar 2020 • Gabriel F. N. Gonçalves, Assen Batchvarov, Yuyi Liu, Yuxin Liu, Lachlan Mason, Indranil Pan, Omar K. Matar
In chemical process engineering, surrogate models of complex systems are often necessary for tasks of domain exploration, sensitivity analysis of the design parameters, and optimization.