Concept
Radial basis function kernel
Related publications (43)
High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization
This paper studies kernel ridge regression in high dimensions under covariate shifts and analyzes the role of importance re-weighting. We first derive the asymptotic expansion of high dimensional kernels under covariate shifts. By a bias-variance decomposi ...
2024A new variable shape parameter strategy for RBF approximation using neural networks
The choice of the shape parameter highly effects the behaviour of radial basis function (RBF) approximations, as it needs to be selected to balance between the ill-conditioning of the interpolation matrix and high accuracy. In this paper, we demonstrate ho ...
PERGAMON-ELSEVIER SCIENCE LTD2023Implicit Distance Functions: Learning and Applications in Control
Aude Billard, Mikhail Koptev, Nadia Barbara Figueroa Fernandez
This paper describes a novel approach to learn an implicit, differentiable distance function for arbitrary configurations of a robotic manipulator used for reactive control. By exploiting GPU processing, we efficiently query the learned collision represent ...
2022Multiscale Representation Learning of Graph Data With Node Affinity
Pascal Frossard, Chenglin Li, Xing Gao
Graph neural networks have emerged as a popular and powerful tool for learning hierarchical representation of graph data. In complement to graph convolution operators, graph pooling is crucial for extracting hierarchical representation of data in graph neu ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems
Pascal Fua, Mathieu Salzmann, Zheng Dang, Kwang Moo Yi, Fei Wang, Yinlin Hu
Many classical Computer Vision problems, such as essential matrix computation and pose estimation from 3D to 2D correspondences, can be tackled by solving a linear least-square problem, which can be done by finding the eigenvector corresponding to the smal ...
IEEE COMPUTER SOC2021Scaling up Kernel Ridge Regression via Locality Sensitive Hashing
Mikhail Kapralov, Amir Zandieh, Navid Nouri
Random binning features, introduced in the seminal paper of Rahimi and Recht '07, are an efficient method for approximating a kernel matrix using locality sensitive hashing. Random binning features provide a very simple and efficient way to approximate the ...
ADDISON-WESLEY PUBL CO2020