Sparse Gaussian Processes with Spherical Harmonic Features Revisited
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Abstract (arXiv)
We revisit the Gaussian process model with spherical harmonic features and study connections between the associated RKHS, its eigenstructure and deep models. Based on this, we introduce a new class of kernels which correspond to deep models of continuous depth. In our formulation, depth can be estimated as a kernel hyper-parameter by optimizing the evidence lower bound. Further, we introduce sparseness in the eigenbasis by variational learning of the spherical harmonic phases. This enables scaling to larger input dimensions than previously, while also allowing for learning of high frequency variations. We validate our approach on machine learning benchmark datasets.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 11 of its 16 references that are in the map. First posted on arXiv 28 Mar 2023.
Builds on
11 of 16 references.
References · 11
- 2018Neural Tangent Kernel: Convergence and Generalization in Neural Networkscited by 2,597
- 2013Gaussian Processes for Big Datacited by 377
- 2018Gaussian Process Behaviour in Wide Deep Neural Networkscited by 314
- 2020Tensor Programs II: Neural Tangent Kernel for Any Architecturecited by 139
- 2018Deep Convolutional Networks as shallow Gaussian Processescited by 137
- 2020Deep Equals Shallow for ReLU Networks in Kernel Regimescited by 83
- 2020Sparse Gaussian Processes with Spherical Harmonic Featurescited by 43
- 2021Approximation and Learning with Deep Convolutional Models: a Kernel Perspectivecited by 31
- 2021Deep Neural Networks as Point Estimates for Deep Gaussian Processescited by 26
- 2021Spectral Analysis of the Neural Tangent Kernel for Deep Residual Networkscited by 20
- 2021Scalable Variational Gaussian Processes via Harmonic Kernel Decompositioncited by 8
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Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.