{"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-structure-learning-with-stochastic","title":"Neural Structure Learning with Stochastic Differential Equations","arxiv_id":"2311.03309","date":"2023-11-06","proceeding":null,"authors":["Benjie Wang","Joel Jennings","Wenbo Gong"],"abstract":"Discovering the underlying relationships among variables from temporal observations has been a longstanding challenge in numerous scientific disciplines, including biology, finance, and climate science. The dynamics of such systems are often best described using continuous-time stochastic processes. Unfortunately, most existing structure learning approaches assume that the underlying process evolves in discrete-time and/or observations occur at regular time intervals. These mismatched assumptions can often lead to incorrect learned structures and models. In this work, we introduce a novel structure learning method, SCOTCH, which combines neural stochastic differential equations (SDE) with variational inference to infer a posterior distribution over possible structures. This continuous-time approach can naturally handle both learning from and predicting observations at arbitrary time points. Theoretically, we establish sufficient conditions for an SDE and SCOTCH to be structurally identifiable, and prove its consistency under infinite data limits. Empirically, we demonstrate that our approach leads to improved structure learning performance on both synthetic and real-world datasets compared to relevant baselines under regular and irregular sampling intervals.","url_abs":"https://arxiv.org/abs/2311.03309v2","url_pdf":"https://arxiv.org/pdf/2311.03309v2.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":[],"tasks":[{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[{"method_slug":"variational-inference","method_name":"Variational Inference"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2311.03309","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.03309"}},"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":"deterministic:regex_extraction","url":"https://github.com/jakobrunge/tigramite","reach":{"status":"ok","spdx":"GPL-3.0"}},{"provenance":"deterministic:regex_extraction","url":"https://github.com/microsoft/causica","reach":null},{"provenance":"deterministic:regex_extraction","url":"https://github.com/jarrycyx/unn","reach":null}],"summary":{"ran":1,"unverified":3},"by_repo_kind":{"found_in_text":{"samples":4,"ran":1,"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":"5ea85801e9cca059","entry":"LocalConv1D","repo":"jarrycyx/unn","repo_kind":"found_in_text","path":"CUTS_Plus/model/cuts_plus_net.py","file_url":"https://github.com/jarrycyx/unn/blob/HEAD/CUTS_Plus/model/cuts_plus_net.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"5ea85801e9cca059"}},{"code_sha256_prefix":"ca13c70f191932ec","entry":"CUTS_Plus_Net","repo":"jarrycyx/unn","repo_kind":"found_in_text","path":"CUTS_Plus/model/cuts_plus_net.py","file_url":"https://github.com/jarrycyx/unn/blob/HEAD/CUTS_Plus/model/cuts_plus_net.py","link_basis":"first_harvest_node","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":"ca13c70f191932ec"}},{"code_sha256_prefix":"e09ef2379cf07373","entry":"GRUCell","repo":"jarrycyx/unn","repo_kind":"found_in_text","path":"CUTS_Plus/model/cuts_plus_net.py","file_url":"https://github.com/jarrycyx/unn/blob/HEAD/CUTS_Plus/model/cuts_plus_net.py","link_basis":"first_harvest_node","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":"e09ef2379cf07373"}},{"code_sha256_prefix":"491e2661437d47e2","entry":"MPNN","repo":"jarrycyx/unn","repo_kind":"found_in_text","path":"CUTS_Plus/model/cuts_plus_net.py","file_url":"https://github.com/jarrycyx/unn/blob/HEAD/CUTS_Plus/model/cuts_plus_net.py","link_basis":"first_harvest_node","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":"491e2661437d47e2"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}