Probability-Generating Function Kernels for Spherical Data
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Abstract (arXiv)
Probability-generating function (PGF) kernels are introduced, which constitute a class of kernels supported on the unit hypersphere, for the purposes of spherical data analysis. PGF kernels generalize RBF kernels in the context of spherical data. The properties of PGF kernels are studied. A semi-parametric learning algorithm is introduced to enable the use of PGF kernels with spherical data.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 19 references that are in the map. First posted on arXiv 1 Dec 2021.
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References · 7
- 2018GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Accelerationcited by 609
- 2015Deep Kernel Learningcited by 445
- 2016Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivitycited by 138
- 2018Dirichlet-based Gaussian Processes for Large-scale Calibrated Classificationcited by 51
- 2019Octree guided CNN with Spherical Kernels for 3D Point Cloudscited by 50
- 2017Exact heat kernel on a hypersphere and its applications in kernel SVMcited by 7
- 2020Mehler's Formula, Branching Process, and Compositional Kernels of Deep Neural Networkscited by 4
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