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The NKN architecture is based on the composition rules for kernels, so\nthat each unit of the network corresponds to a valid kernel. It can compactly\napproximate compositional kernel structures such as those used by the Automatic\nStatistician (Lloyd et al., 2014), but because the architecture is\ndifferentiable, it is end-to-end trainable with gradient-based optimization. We\nshow that the NKN is universal for the class of stationary kernels. Empirically\nwe demonstrate pattern discovery and extrapolation abilities of NKN on several\ntasks that depend crucially on identifying the underlying structure, including\ntime series and texture extrapolation, as well as Bayesian optimization.","url_abs":"http://arxiv.org/abs/1806.04326v3","url_pdf":"http://arxiv.org/pdf/1806.04326v3.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":"differentiable-compositional-kernel-learning","repo_url":"https://github.com/hughsalimbeni/bayesian_benchmarks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"differentiable-compositional-kernel-learning","repo_url":"https://github.com/secondmind-labs/bayesian_benchmarks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"differentiable-compositional-kernel-learning","repo_url":"https://github.com/ssydasheng/Neural-Kernel-Network","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"differentiable-compositional-kernel-learning","repo_url":"https://github.com/thjashin/spectral-stein-grad","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"},{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":null,"task_name":"valid"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.04326","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.04326"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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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