Zobrazeno 1 - 10
of 472
pro vyhledávání: '"ZHANG, Yinghao"'
The obesity phenomenon, known as the heavy issue, is a leading cause of preventable chronic diseases worldwide. Traditional calorie estimation tools often rely on specific data formats or complex pipelines, limiting their practicality in real-world s
Externí odkaz:
http://arxiv.org/abs/2412.09936
Autor:
Zhang, Yinghao, Hu, Yue
Low-rank regularization-based deep unrolling networks have achieved remarkable success in various inverse imaging problems (IIPs). However, the singular value decomposition (SVD) is non-differentiable when duplicated singular values occur, leading to
Externí odkaz:
http://arxiv.org/abs/2411.14141
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
Autor:
Zhang, Yinghao a, b, Chen, Delu b, Xia, Yifan a, b, c, Guo, Mengjia b, Chao, Kefu d, ⁎, Li, Shuhan b, Ma, Shifan b, Wang, Xin a, b, c, e, ⁎⁎
Publikováno v:
In Nano Energy January 2025 133
Autor:
Zhang, Yinghao a, Li, Zhanying a, ⁎, Kong, Lingyan a, Xu, Hao a, Shen, Houwen a, Chen, Ming b
Publikováno v:
In Journal of Energy Storage 1 January 2025 105