{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/neural-non-stationary-spectral-kernel","title":"Neural Non-Stationary Spectral Kernel","arxiv_id":"1811.10978","date":"2018-11-27","proceeding":null,"authors":["Sami Remes","Markus Heinonen","Samuel Kaski"],"abstract":"Standard kernels such as Mat\\'ern or RBF kernels only encode simple monotonic\ndependencies within the input space. Spectral mixture kernels have been\nproposed as general-purpose, flexible kernels for learning and discovering more\ncomplicated patterns in the data. Spectral mixture kernels have recently been\ngeneralized into non-stationary kernels by replacing the mixture weights,\nfrequency means and variances by input-dependent functions. These functions\nhave also been modelled as Gaussian processes on their own. In this paper we\npropose modelling the hyperparameter functions with neural networks, and\nprovide an experimental comparison between the stationary spectral mixture and\nthe two non-stationary spectral mixtures. Scalable Gaussian process inference\nis implemented within the sparse variational framework for all the kernels\nconsidered. We show that the neural variant of the kernel is able to achieve\nthe best performance, among alternatives, on several benchmark datasets.","url_abs":"http://arxiv.org/abs/1811.10978v1","url_pdf":"http://arxiv.org/pdf/1811.10978v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"neural-non-stationary-spectral-kernel","repo_url":"https://github.com/sremes/nssm-gp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1811.10978","atlas_url":"https://app.syntology.ai/?focus=1811.10978","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.10978"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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