Zobrazeno 1 - 10
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pro vyhledávání: '"A, Shiwei"'
We present a generalization of the phaseless auxiliary-field quantum Monte Carlo (AFQMC) method to cavity quantum-electrodynamical (QED) matter systems. The method can be formulated in both the Coulomb and the dipole gauge. We verify its accuracy by
Externí odkaz:
http://arxiv.org/abs/2410.18838
Autor:
Wei, Yujie, Zhang, Shiwei, Yuan, Hangjie, Wang, Xiang, Qiu, Haonan, Zhao, Rui, Feng, Yutong, Liu, Feng, Huang, Zhizhong, Ye, Jiaxin, Zhang, Yingya, Shan, Hongming
Recent advances in customized video generation have enabled users to create videos tailored to both specific subjects and motion trajectories. However, existing methods often require complicated test-time fine-tuning and struggle with balancing subje
Externí odkaz:
http://arxiv.org/abs/2410.13830
Autor:
Wang, Ke, Zhu, Jiahui, Ren, Minjie, Liu, Zeming, Li, Shiwei, Zhang, Zongye, Zhang, Chenkai, Wu, Xiaoyu, Zhan, Qiqi, Liu, Qingjie, Wang, Yunhong
The success of Large Language Models (LLMs) is inherently linked to the availability of vast, diverse, and high-quality data for training and evaluation. However, the growth rate of high-quality data is significantly outpaced by the expansion of trai
Externí odkaz:
http://arxiv.org/abs/2410.12896
Autor:
Hu, Shiwei, Xiao, Tianbai, Han, Mingshuo, Li, Zuoxu, Oterkus, Erkan, Oterkus, Selda, Zhang, Yonghao
Understanding the quasi-static fracture formation and evolution is essential for assessing the mechanical properties and structural load-bearing capacity of materials. Peridynamics (PD) provides an effective computational method to depict fracture me
Externí odkaz:
http://arxiv.org/abs/2410.12552
Autor:
Tan, Shuai, Gong, Biao, Wang, Xiang, Zhang, Shiwei, Zheng, Dandan, Zheng, Ruobing, Zheng, Kecheng, Chen, Jingdong, Yang, Ming
Character image animation, which generates high-quality videos from a reference image and target pose sequence, has seen significant progress in recent years. However, most existing methods only apply to human figures, which usually do not generalize
Externí odkaz:
http://arxiv.org/abs/2410.10306
Recent work on pruning large language models (LLMs) has shown that one can eliminate a large number of parameters without compromising performance, making pruning a promising strategy to reduce LLM model size. Existing LLM pruning strategies typicall
Externí odkaz:
http://arxiv.org/abs/2410.10912
This paper investigates the under-explored area of low-rank weight training for large-scale Conformer-based speech recognition models from scratch. Our study demonstrates the viability of this training paradigm for such models, yielding several notab
Externí odkaz:
http://arxiv.org/abs/2410.07771
Autor:
Bandari, Abhinav, Yin, Lu, Hsieh, Cheng-Yu, Jaiswal, Ajay Kumar, Chen, Tianlong, Shen, Li, Krishna, Ranjay, Liu, Shiwei
Network pruning has emerged as a potential solution to make LLMs cheaper to deploy. However, existing LLM pruning approaches universally rely on the C4 dataset as the calibration data for calculating pruning scores, leaving its optimality unexplored.
Externí odkaz:
http://arxiv.org/abs/2410.07461
The two-dimensional (2D) homogeneous electron gas (HEG) is a fundamental model in quantum many-body physics. It is important to theoretical and computational studies, where exchange-correlation energies computed in it serve as the foundation for dens
Externí odkaz:
http://arxiv.org/abs/2410.07445
Autor:
Zhao, Rui, Yuan, Hangjie, Wei, Yujie, Zhang, Shiwei, Gu, Yuchao, Ran, Lingmin, Wang, Xiang, Wu, Zhangjie, Zhang, Junhao, Zhang, Yingya, Shou, Mike Zheng
Recent advancements in generation models have showcased remarkable capabilities in generating fantastic content. However, most of them are trained on proprietary high-quality data, and some models withhold their parameters and only provide accessible
Externí odkaz:
http://arxiv.org/abs/2410.07133