{"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/deeper-connections-between-neural-networks","title":"Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning","arxiv_id":"1902.10350","date":"2019-02-27","proceeding":null,"authors":["Evgenii Tsymbalov","Sergei Makarychev","Alexander Shapeev","Maxim Panov"],"abstract":"Active learning methods for neural networks are usually based on greedy\ncriteria which ultimately give a single new design point for the evaluation.\nSuch an approach requires either some heuristics to sample a batch of design\npoints at one active learning iteration, or retraining the neural network after\nadding each data point, which is computationally inefficient. Moreover,\nuncertainty estimates for neural networks sometimes are overconfident for the\npoints lying far from the training sample. In this work we propose to\napproximate Bayesian neural networks (BNN) by Gaussian processes, which allows\nus to update the uncertainty estimates of predictions efficiently without\nretraining the neural network, while avoiding overconfident uncertainty\nprediction for out-of-sample points. In a series of experiments on real-world\ndata including large-scale problems of chemical and physical modeling, we show\nsuperiority of the proposed approach over the state-of-the-art methods.","url_abs":"http://arxiv.org/abs/1902.10350v1","url_pdf":"http://arxiv.org/pdf/1902.10350v1.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":"deeper-connections-between-neural-networks","repo_url":"https://github.com/LukasErlenbach/active_learning_bnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"active-learning","task_name":"Active Learning"},{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1902.10350","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.10350"}},"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/LukasErlenbach/active_learning_bnn","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":"b1f3fa38d1f4d087","entry":"network","repo":"LukasErlenbach/active_learning_bnn","repo_kind":"listed","path":"source/models/bnn_model.py","file_url":"https://github.com/LukasErlenbach/active_learning_bnn/blob/HEAD/source/models/bnn_model.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":"b1f3fa38d1f4d087"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}