{"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/a-scalable-end-to-end-gaussian-process","title":"A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification","arxiv_id":"1606.04443","date":"2016-06-14","proceeding":"NeurIPS 2016 12","authors":["Steven Cheng-Xian Li","Benjamin Marlin"],"abstract":"We present a general framework for classification of sparse and\nirregularly-sampled time series. The properties of such time series can result\nin substantial uncertainty about the values of the underlying temporal\nprocesses, while making the data difficult to deal with using standard\nclassification methods that assume fixed-dimensional feature spaces. To address\nthese challenges, we propose an uncertainty-aware classification framework\nbased on a special computational layer we refer to as the Gaussian process\nadapter that can connect irregularly sampled time series data to any black-box\nclassifier learnable using gradient descent. We show how to scale up the\nrequired computations based on combining the structured kernel interpolation\nframework and the Lanczos approximation method, and how to discriminatively\ntrain the Gaussian process adapter in combination with a number of classifiers\nend-to-end using backpropagation.","url_abs":"http://arxiv.org/abs/1606.04443v2","url_pdf":"http://arxiv.org/pdf/1606.04443v2.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":"a-scalable-end-to-end-gaussian-process","repo_url":"https://github.com/steveli/gp-adapter","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-classification","task_name":"Time Series Classification"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1606.04443","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1606.04443"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/steveli/gp-adapter","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":1},"by_repo_kind":{"listed":{"samples":1,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"95e644b77d25a0a5","entry":"kernel","repo":"steveli/gp-adapter","repo_kind":"listed","path":"gp_kernel.py","file_url":"https://github.com/steveli/gp-adapter/blob/HEAD/gp_kernel.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"95e644b77d25a0a5"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}