{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:67N2VXKN5IPPD4CYTEBHISSELN","short_pith_number":"pith:67N2VXKN","schema_version":"1.0","canonical_sha256":"f7dbaadd4dea1ef1f0589902744a445b65fc61ec85d321b898e71e49db3f7cfc","source":{"kind":"arxiv","id":"2011.03902","version":4},"attestation_state":"computed","paper":{"title":"Learning Neural Event Functions for Ordinary Differential Equations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Brandon Amos, Maximilian Nickel, Ricky T. Q. Chen","submitted_at":"2020-11-08T04:33:54Z","abstract_excerpt":"The existing Neural ODE formulation relies on an explicit knowledge of the termination time. We extend Neural ODEs to implicitly defined termination criteria modeled by neural event functions, which can be chained together and differentiated through. Neural Event ODEs are capable of modeling discrete and instantaneous changes in a continuous-time system, without prior knowledge of when these changes should occur or how many such changes should exist. We test our approach in modeling hybrid discrete- and continuous- systems such as switching dynamical systems and collision in multi-body systems"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2011.03902","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-11-08T04:33:54Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"513a3df319473d61dbae36afa12fa30a15031e674d51a6116d79bd9ef1b535fd","abstract_canon_sha256":"d5f47ccd64ac71811216cb9a03160eae8201374e9088230e5614a4138c8cda49"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:26:14.308546Z","signature_b64":"HneMEtYQo9T9GwQIwFcUYxX5xYHrul7P4M/yUjueN1TPdugP+34DnXyXt/0mPNNXBgTHJYiNTebgEnmV9wbBCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f7dbaadd4dea1ef1f0589902744a445b65fc61ec85d321b898e71e49db3f7cfc","last_reissued_at":"2026-07-05T03:26:14.307927Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:26:14.307927Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Neural Event Functions for Ordinary Differential Equations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Brandon Amos, Maximilian Nickel, Ricky T. Q. Chen","submitted_at":"2020-11-08T04:33:54Z","abstract_excerpt":"The existing Neural ODE formulation relies on an explicit knowledge of the termination time. We extend Neural ODEs to implicitly defined termination criteria modeled by neural event functions, which can be chained together and differentiated through. Neural Event ODEs are capable of modeling discrete and instantaneous changes in a continuous-time system, without prior knowledge of when these changes should occur or how many such changes should exist. We test our approach in modeling hybrid discrete- and continuous- systems such as switching dynamical systems and collision in multi-body systems"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.03902","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2011.03902/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2011.03902","created_at":"2026-07-05T03:26:14.308007+00:00"},{"alias_kind":"arxiv_version","alias_value":"2011.03902v4","created_at":"2026-07-05T03:26:14.308007+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.03902","created_at":"2026-07-05T03:26:14.308007+00:00"},{"alias_kind":"pith_short_12","alias_value":"67N2VXKN5IPP","created_at":"2026-07-05T03:26:14.308007+00:00"},{"alias_kind":"pith_short_16","alias_value":"67N2VXKN5IPPD4CY","created_at":"2026-07-05T03:26:14.308007+00:00"},{"alias_kind":"pith_short_8","alias_value":"67N2VXKN","created_at":"2026-07-05T03:26:14.308007+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.10596","citing_title":"Embedding Hybrid Systems into Continuous Latent Vector Fields","ref_index":76,"is_internal_anchor":false},{"citing_arxiv_id":"2606.07798","citing_title":"Reconstructing and forecasting disease trajectories of patients with Alzheimer's disease using routine data in resource-constrained settings","ref_index":36,"is_internal_anchor":false},{"citing_arxiv_id":"2605.24275","citing_title":"Learning regime-dependent governing equations: A symbolic decision tree approach","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2509.21280","citing_title":"Model reduction of parametric ordinary differential equations via autoencoders: representation properties and convergence analysis","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09196","citing_title":"RigidFormer: Learning Rigid Dynamics using Transformers","ref_index":8,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/67N2VXKN5IPPD4CYTEBHISSELN","json":"https://pith.science/pith/67N2VXKN5IPPD4CYTEBHISSELN.json","graph_json":"https://pith.science/api/pith-number/67N2VXKN5IPPD4CYTEBHISSELN/graph.json","events_json":"https://pith.science/api/pith-number/67N2VXKN5IPPD4CYTEBHISSELN/events.json","paper":"https://pith.science/paper/67N2VXKN"},"agent_actions":{"view_html":"https://pith.science/pith/67N2VXKN5IPPD4CYTEBHISSELN","download_json":"https://pith.science/pith/67N2VXKN5IPPD4CYTEBHISSELN.json","view_paper":"https://pith.science/paper/67N2VXKN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2011.03902&json=true","fetch_graph":"https://pith.science/api/pith-number/67N2VXKN5IPPD4CYTEBHISSELN/graph.json","fetch_events":"https://pith.science/api/pith-number/67N2VXKN5IPPD4CYTEBHISSELN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/67N2VXKN5IPPD4CYTEBHISSELN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/67N2VXKN5IPPD4CYTEBHISSELN/action/storage_attestation","attest_author":"https://pith.science/pith/67N2VXKN5IPPD4CYTEBHISSELN/action/author_attestation","sign_citation":"https://pith.science/pith/67N2VXKN5IPPD4CYTEBHISSELN/action/citation_signature","submit_replication":"https://pith.science/pith/67N2VXKN5IPPD4CYTEBHISSELN/action/replication_record"}},"created_at":"2026-07-05T03:26:14.308007+00:00","updated_at":"2026-07-05T03:26:14.308007+00:00"}