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pro vyhledávání: '"Yang, YunFei"'
Autor:
Yang, Yunfei, Qi, Zhenghao, Wu, Honghuan, Song, Qi, Zhang, Tieyao, Li, Hao, Tu, Yimin, Zhan, Kaiqiao, Wang, Ben
Video recommender systems (RSs) have gained increasing attention in recent years. Existing mainstream RSs focus on optimizing the matching function between users and items. However, we noticed that users frequently encounter playback issues such as s
Externí odkaz:
http://arxiv.org/abs/2410.05863
Autor:
Yang, Yunfei
This paper studies the problem of how efficiently functions in the Sobolev spaces $\mathcal{W}^{s,q}([0,1]^d)$ and Besov spaces $\mathcal{B}^s_{q,r}([0,1]^d)$ can be approximated by deep ReLU neural networks with width $W$ and depth $L$, when the err
Externí odkaz:
http://arxiv.org/abs/2409.00901
We study approximation and learning capacities of convolutional neural networks (CNNs) with one-side zero-padding and multiple channels. Our first result proves a new approximation bound for CNNs with certain constraint on the weights. Our second res
Externí odkaz:
http://arxiv.org/abs/2403.16459
Publikováno v:
陆军军医大学学报, Vol 46, Iss 22, Pp 2485-2492 (2024)
Objective To knock out cdc42 gene in zebrafish using CRISPR/Cas9 technology, and investigate the effect of cdc42 on early osteochondral development. Methods After the conservation of cdc42 gene sequence of different species was analyzed by multiple s
Externí odkaz:
https://doaj.org/article/5c846fae8cbc4f2ebbde4b3dbb769fb5
Autor:
Yang, Yunfei, Zhou, Ding-Xuan
Publikováno v:
Journal of Machine Learning Research, 25(165):1-35, 2024
It is shown that over-parameterized neural networks can achieve minimax optimal rates of convergence (up to logarithmic factors) for learning functions from certain smooth function classes, if the weights are suitably constrained or regularized. Spec
Externí odkaz:
http://arxiv.org/abs/2306.08321
Autor:
Yang, Yunfei, Zhou, Ding-Xuan
We study the approximation capacity of some variation spaces corresponding to shallow ReLU$^k$ neural networks. It is shown that sufficiently smooth functions are contained in these spaces with finite variation norms. For functions with less smoothne
Externí odkaz:
http://arxiv.org/abs/2304.01561
This paper analyzes the convergence rate of a deep Galerkin method for the weak solution (DGMW) of second-order elliptic partial differential equations on $\mathbb{R}^d$ with Dirichlet, Neumann, and Robin boundary conditions, respectively. In DGMW, a
Externí odkaz:
http://arxiv.org/abs/2302.02405
Autor:
Liu, Wen1 (AUTHOR), Yang, Yunfei1 (AUTHOR), Xiao, Gongyi1 (AUTHOR), Tang, Chun1 (AUTHOR), Deng, Zhongliang1 (AUTHOR) zhongliang_deng@sina.com
Publikováno v:
Journal of Analytical Science & Technology. 11/7/2024, Vol. 15 Issue 1, p1-7. 7p.
Autor:
Li, Zhen, Yang, Yunfei
We study the uniform approximation of echo state networks with randomly generated internal weights. These models, in which only the readout weights are optimized during training, have made empirical success in learning dynamical systems. Recent resul
Externí odkaz:
http://arxiv.org/abs/2206.05669
Autor:
Yang, Yunfei
We study how well generative adversarial networks (GAN) learn probability distributions from finite samples by analyzing the convergence rates of these models. Our analysis is based on a new oracle inequality that decomposes the estimation error of G
Externí odkaz:
http://arxiv.org/abs/2205.12601