Zobrazeno 1 - 10
of 605
pro vyhledávání: '"Lin Yuanyuan"'
Large language models (LLMs) have excelled in various natural language processing tasks, but challenges in interpretability and trustworthiness persist, limiting their use in high-stakes fields. Causal discovery offers a promising approach to improve
Externí odkaz:
http://arxiv.org/abs/2406.04598
Online statistical inference facilitates real-time analysis of sequentially collected data, making it different from traditional methods that rely on static datasets. This paper introduces a novel approach to online inference in high-dimensional gene
Externí odkaz:
http://arxiv.org/abs/2405.18284
Semi-supervised learning has received increasingly attention in statistics and machine learning. In semi-supervised learning settings, a labeled data set with both outcomes and covariates and an unlabeled data set with covariates only are collected.
Externí odkaz:
http://arxiv.org/abs/2402.15365
In this paper, we propose a new and unified approach for nonparametric regression and conditional distribution learning. Our approach simultaneously estimates a regression function and a conditional generator using a generative learning framework, wh
Externí odkaz:
http://arxiv.org/abs/2306.15163
We study the properties of differentiable neural networks activated by rectified power unit (RePU) functions. We show that the partial derivatives of RePU neural networks can be represented by RePUs mixed-activated networks and derive upper bounds fo
Externí odkaz:
http://arxiv.org/abs/2305.00608
Autor:
Lin, Yuanyuan1,2 (AUTHOR) 2001210192@email.cugb.edu.cn, Li, Hui1,3 (AUTHOR) jinglh@radi.ac.cn, Jing, Linhai1,3 (AUTHOR) hfding@cbas.ac.cn, Ding, Haifeng1,3 (AUTHOR), Tian, Shufang4 (AUTHOR) sftian@cugb.edu.cn
Publikováno v:
Remote Sensing. Nov2024, Vol. 16 Issue 21, p3920. 25p.
We propose a nonparametric quantile regression method using deep neural networks with a rectified linear unit penalty function to avoid quantile crossing. This penalty function is computationally feasible for enforcing non-crossing constraints in mul
Externí odkaz:
http://arxiv.org/abs/2210.10161
We propose a mutual information-based sufficient representation learning (MSRL) approach, which uses the variational formulation of the mutual information and leverages the approximation power of deep neural networks. MSRL learns a sufficient represe
Externí odkaz:
http://arxiv.org/abs/2207.10772
We propose a penalized nonparametric approach to estimating the quantile regression process (QRP) in a nonseparable model using rectifier quadratic unit (ReQU) activated deep neural networks and introduce a novel penalty function to enforce non-cross
Externí odkaz:
http://arxiv.org/abs/2207.10442
Autor:
Zhou, Mengyuan, Lin, Yuanyuan, Chen, Haiyan, Zhao, Mei, Zeng, Yuteng, Hu, Xiaoxiao, Tang, Puxian, Fu, Yuxuan, Wei, Lin, Han, Liang
Publikováno v:
In Journal of Controlled Release November 2024 375:116-126