Zobrazeno 1 - 10
of 122
pro vyhledávání: '"Wang, Chunnan"'
Publikováno v:
In Materials Today Physics November 2024 48
Automatic Time Series Forecasting (TSF) model design which aims to help users to efficiently design suitable forecasting model for the given time series data scenarios, is a novel research topic to be urgently solved. In this paper, we propose AutoTS
Externí odkaz:
http://arxiv.org/abs/2203.14169
Model compression methods can reduce model complexity on the premise of maintaining acceptable performance, and thus promote the application of deep neural networks under resource constrained environments. Despite their great success, the selection o
Externí odkaz:
http://arxiv.org/abs/2201.09884
Trajectory Prediction (TP) is an important research topic in computer vision and robotics fields. Recently, many stochastic TP models have been proposed to deal with this problem and have achieved better performance than the traditional models with d
Externí odkaz:
http://arxiv.org/abs/2201.02941
Publikováno v:
In Applied Soft Computing September 2024 162
Current GNN-oriented NAS methods focus on the search for different layer aggregate components with shallow and simple architectures, which are limited by the 'over-smooth' problem. To further explore the benefits from structural diversity and depth o
Externí odkaz:
http://arxiv.org/abs/2109.10047
Autor:
Wang, Chunnan
Freund [1961] introduced a bivariate extension of the exponential distribution that provides a model in which the exponential residual lifetime of one component depends on the working status of another component. We define and study an extension of t
Externí odkaz:
http://hdl.handle.net/10150/284128
Autor:
Sha, Zhou, Gao, Xiaochun, Wang, Yijie, Guan, Xiaotian, Zhang, Sihao, Zhao, Jingru, Wang, Chunnan, Sun, Shuqing
Publikováno v:
In Colloids and Surfaces A: Physicochemical and Engineering Aspects 20 April 2024 687
Recently, some Neural Architecture Search (NAS) techniques are proposed for the automatic design of Graph Convolutional Network (GCN) architectures. They bring great convenience to the use of GCN, but could hardly apply to the Federated Learning (FL)
Externí odkaz:
http://arxiv.org/abs/2104.04141
In recent years, many spatial-temporal graph convolutional network (STGCN) models are proposed to deal with the spatial-temporal network data forecasting problem. These STGCN models have their own advantages, i.e., each of them puts forward many effe
Externí odkaz:
http://arxiv.org/abs/2010.07474