虽然已有文献提出了基于循环神经网络(RNN)、图神经网络(GNN)或Transformer等复杂架构,但也有一种基于多层感知器(MLPs)的方法,其结构简单、复杂度低且性能优越。然而,大多数基于MLP的预测方法存在点对点映射和信息瓶颈等问题,这严重制约了预测性能。为了解决这一问题,我们探索了一种新的方向,即将MLPs应用于频域进行时间...
Frequency-domain parameter estimation of general multi-rate systems 星级: 12 页 Frequency-Domain Receivers for Rate-1 Space-Time Block Codes 星级: 7 页 Frequency-Domain 星级: 117 页 frequency-domain 星级: 83 页 frequency-domain 星级: 12 页 FREQUENCY-DOMAIN 星级: 97 页 frequency-doma...
This repo is the official Pytorch implementation of"Frequency-domain MLPs are More Effective Learners in Time Series Forecasting". forecasting python run_longExp.py draw the visualization python weight_plot.py Citation If you find this repo useful, please cite our paper. ...
traditional multi-layer perceptrons (MLPs) with significantly fewer parameters. They are better at learning composite structures and univariate functions, making them more effective in capturing and expressing complex relationships in certain types of data and problems compared to MLPs. This is ...