Convolution Kernels
Convolution Kernels is a mathematical function or matrix in the context of kernel methods that enables local feature extraction and translation-equivariant transformations by applying weighted local operations across structured data such as images, time series, or spatial domains.
1.8K
Publications
148.6K
Citations
5.9K
Authors
1.5K
Institutions
Publications per year
2017–2026
1.2K
Parents
1.8K
Michael D. Abràmoff, Paulo Magalhães, Sunanda J. Ram · Data Archiving and Networked Services (DANS) · 2004 · 11.9K citations · Full text
Learning Spatiotemporal Features with 3D Convolutional Networks
Du Tran, Lubomir Bourdev, Rob Fergus et al. · 2015 · 9.7K citations
ECO: Efficient Convolution Operators for Tracking
Martin Danelljan, Goutam Bhat, Fahad Shahbaz Khan et al. · 2017 · 2.8K citations · Full text
Striving for Simplicity: The All Convolutional Net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox et al. · arXiv (Cornell University) · 2014 · 2.6K citations · Full text
1–5 of 1.8K
5.9K
11
h-index
10
h-index
10
h-index
8
h-index
7
h-index
1–5 of 5.9K
1.5K

Chinese Academy of Sciences
Beijing, China
30 h-index
145 publications
7.2K citations
Tsinghua University
Beijing, China
21 h-index
118 publications
15.2K citations

Shanghai Jiao Tong University
Shanghai, China
20 h-index
59 publications
4.3K citations
University of Chinese Academy of Sciences
Beijing, China
19 h-index
74 publications
5.3K citations
Stanford University
Stanford, United States
19 h-index
50 publications
6.1K citations

Harbin Institute of Technology
Harbin, China
17 h-index
50 publications
2.7K citations

Sun Yat-sen University
Guangzhou, China
17 h-index
46 publications
2.6K citations

Centre National de la Recherche Scientifique
Paris, France
16 h-index
46 publications
2.8K citations
1–8 of 1.5K
Repository · 166 publications · 20.3K citations
54
h-index
50 publications · 2.5K citations
27
h-index
30 publications · 1.8K citations
23
h-index
23 publications · 2.1K citations
20
h-index
18 publications · 730 citations
15
h-index
1–5