Zobrazeno 1 - 10
of 7 231
pro vyhledávání: '"Wang, Can"'
Generating and inserting new objects into 3D content is a compelling approach for achieving versatile scene recreation. Existing methods, which rely on SDS optimization or single-view inpainting, often struggle to produce high-quality results. To add
Externí odkaz:
http://arxiv.org/abs/2409.16938
Autor:
Chen, Wang, Hu, Mengli, Zong, Junyu, Xie, Xuedong, Ren, Wei, Meng, Qinghao, Yu, Fan, Tian, Qichao, Jin, Shaoen, Qiu, Xiaodong, Wang, Kaili, Wang, Can, Liu, Junwei, Li, Fang-Sen, Wang, Li, Zhang, Yi
The transition metal dichalcogenides (TMDCs) with a 1T' structural phase are predicted to be two-dimensional topological insulators at zero temperature. Although the quantized edge conductance of 1T'-WTe$_2$ has been confirmed to survive up to 100 K,
Externí odkaz:
http://arxiv.org/abs/2409.09698
Autor:
Lu, Zekun, Chen, Feng, Guo, J. H., Ding, M. D., Wang, Can, Yu, Haocheng, Ni, Y. W., Xia, Chun
The periodic coronal rain and in-phase radiative intensity pulsations have been observed in multiple wavelengths in recent years. However, due to the lack of three-dimensional coronal magnetic fields and thermodynamic data in observations, it remains
Externí odkaz:
http://arxiv.org/abs/2408.16988
Autor:
Wang, Bohao, Liu, Feng, Chen, Jiawei, Wu, Yudi, Lou, Xingyu, Wang, Jun, Feng, Yan, Chen, Chun, Wang, Can
Sequential recommendation systems fundamentally rely on users' historical interaction sequences, which are often contaminated by noisy interactions. Identifying these noisy interactions accurately without additional information is particularly diffic
Externí odkaz:
http://arxiv.org/abs/2408.08208
Recent research on knowledge distillation has increasingly focused on logit distillation because of its simplicity, effectiveness, and versatility in model compression. In this paper, we introduce Refined Logit Distillation (RLD) to address the limit
Externí odkaz:
http://arxiv.org/abs/2408.07703
Automatic furniture layout is long desired for convenient interior design. Leveraging the remarkable visual reasoning capabilities of multimodal large language models (MLLMs), recent methods address layout generation in a static manner, lacking the f
Externí odkaz:
http://arxiv.org/abs/2407.21333
Autor:
Wang, Zhe, Zhou, Sheng, Chen, Jiawei, Zhang, Zhen, Hu, Binbin, Feng, Yan, Chen, Chun, Wang, Can
Learning effective representations for Continuous-Time Dynamic Graphs (CTDGs) has garnered significant research interest, largely due to its powerful capabilities in modeling complex interactions between nodes. A fundamental and crucial requirement f
Externí odkaz:
http://arxiv.org/abs/2407.16959
This paper employs a novel Lie symmetry-based framework to model the intrinsic symmetries within financial market. Specifically, we introduce {\it Lie symmetry net} (LSN), which characterises the Lie symmetry of the differential equations (DE) estima
Externí odkaz:
http://arxiv.org/abs/2406.09189
Autor:
Zhou, Zhiyao, Zhou, Sheng, Mao, Bochao, Chen, Jiawei, Sun, Qingyun, Feng, Yan, Chen, Chun, Wang, Can
To mitigate the suboptimal nature of graph structure, Graph Structure Learning (GSL) has emerged as a promising approach to improve graph structure and boost performance in downstream tasks. Despite the proposal of numerous GSL methods, the progresse
Externí odkaz:
http://arxiv.org/abs/2406.08897
Diffusion-based generative models use stochastic differential equations (SDEs) and their equivalent ordinary differential equations (ODEs) to establish a smooth connection between a complex data distribution and a tractable prior distribution. In thi
Externí odkaz:
http://arxiv.org/abs/2405.11326