Abstract: In this paper, we present a novel convolution theorem which encompasses the well known convolution theorem in (graph) signal processing as well as the one related to time-varying filters.
Adapting to the Stream: An Instance-Attention GNN Method for Irregular Multivariate Time Series Data
DynIMTS replaces static graphs with instance-attention that updates edge weights on the fly, delivering SOTA imputation and P12 classification ...
This repository is an official PyTorch implementation of "Omni-Dimensional Dynamic Convolution", ODConv for short, published by ICLR 2022 as a spotlight. ODConv is a more generalized yet elegant ...
Cardiovascular disease is a term used to describe diseases of the heart and blood vessels. Some of the common types of cardiovascular disease include coronary artery disease, heart attack, and heart ...
This repository contains the implementation of AdaptConv for point cloud analysis. Adaptive Graph Convolution (AdaptConv) is a point cloud convolution operator presented in our ICCV2021 paper. If you ...
Abstract: Convolution and self-attention are two powerful techniques for multisource remote sensing (RS) data fusion that have been widely adopted in Earth observation tasks. However, convolutional ...
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