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pro vyhledávání: '"Zhang, YingHao"'
Estimating causal effects from observational data is challenging, especially in the presence of latent confounders. Much work has been done on addressing this challenge, but most of the existing research ignores the bias introduced by the post-treatm
Externí odkaz:
http://arxiv.org/abs/2408.07219
Autor:
Cheng, Debo, Xie, Yang, Xu, Ziqi, Li, Jiuyong, Liu, Lin, Liu, Jixue, Zhang, Yinghao, Feng, Zaiwen
In causal inference, it is a fundamental task to estimate the causal effect from observational data. However, latent confounders pose major challenges in causal inference in observational data, for example, confounding bias and M-bias. Recent data-dr
Externí odkaz:
http://arxiv.org/abs/2312.05404
Deep unrolling networks that utilize sparsity priors have achieved great success in dynamic magnetic resonance (MR) imaging. The convolutional neural network (CNN) is usually utilized to extract the transformed domain, and then the soft thresholding
Externí odkaz:
http://arxiv.org/abs/2307.09818
Causal inference plays an important role in under standing the underlying mechanisation of the data generation process across various domains. It is challenging to estimate the average causal effect and individual causal effects from observational da
Externí odkaz:
http://arxiv.org/abs/2301.01549
Autor:
Qiu, Zesong, Li, Yuwei, He, Dongming, Zhang, Qixuan, Zhang, Longwen, Zhang, Yinghao, Wang, Jingya, Xu, Lan, Wang, Xudong, Zhang, Yuyao, Yu, Jingyi
Recent years have seen growing interest in 3D human faces modelling due to its wide applications in digital human, character generation and animation. Existing approaches overwhelmingly emphasized on modeling the exterior shapes, textures and skin pr
Externí odkaz:
http://arxiv.org/abs/2209.06423
Autor:
Zhang, Yinghao, Hu, Yue
While the methods exploiting the tensor low-rank prior are booming in high-dimensional data processing and have obtained satisfying performance, their applications in dynamic magnetic resonance (MR) image reconstruction are limited. In this paper, we
Externí odkaz:
http://arxiv.org/abs/2209.03832
While low-rank matrix prior has been exploited in dynamic MR image reconstruction and has obtained satisfying performance, tensor low-rank models have recently emerged as powerful alternative representations for three-dimensional dynamic MR datasets.
Externí odkaz:
http://arxiv.org/abs/2206.00850
Autor:
Zhang, Yinghao, Hu, Yue
Publikováno v:
[C]//2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). IEEE, 2022: 1-4
Low-rank tensor models have been applied in accelerating dynamic magnetic resonance imaging (dMRI). Recently, a new tensor nuclear norm based on t-SVD has been proposed and applied to tensor completion. Inspired by the different properties of the ten
Externí odkaz:
http://arxiv.org/abs/2206.00831
Publikováno v:
In Chemical Engineering Journal 15 October 2024 498
Autor:
Zeng, Qingguo, Zhang, Yinghao, Lei, Xin, Jiang, Ying, Zhuo, Yinuo, Ni, Jiatao, Zhang, Haokun, Li, Zheng, Ai, Yeye, Li, Yongguang
Publikováno v:
In Chemical Engineering Journal 1 October 2024 497