{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:JNJXKQNAUYUXZB2KEPZ2WPPX2H","short_pith_number":"pith:JNJXKQNA","schema_version":"1.0","canonical_sha256":"4b537541a0a6297c874a23f3ab3df7d1d41ca354c03290358f4de3b254a37cbb","source":{"kind":"arxiv","id":"2206.12928","version":1},"attestation_state":"computed","paper":{"title":"Learning neural state-space models: do we need a state estimator?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Dario Piga, Manas Mejari, Marco Forgione","submitted_at":"2022-06-26T17:15:35Z","abstract_excerpt":"In recent years, several algorithms for system identification with neural state-space models have been introduced. Most of the proposed approaches are aimed at reducing the computational complexity of the learning problem, by splitting the optimization over short sub-sequences extracted from a longer training dataset. Different sequences are then processed simultaneously within a minibatch, taking advantage of modern parallel hardware for deep learning. An issue arising in these methods is the need to assign an initial state for each of the sub-sequences, which is required to run simulations a"},"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":"2206.12928","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-26T17:15:35Z","cross_cats_sorted":["cs.AI","cs.SY","eess.SY"],"title_canon_sha256":"9ffc99eb4e14feb8e0b9c0bee85183a06e70652635aa6ca1639fd9414a62d687","abstract_canon_sha256":"984fd38cb2a5163bf04b01a1ffcdbd8826ab28a50c0023b8a55330ac6b4a3604"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:35:00.745396Z","signature_b64":"sp0Qpb5XDT9GEKTC87kIAYMDc2PdG16d+sopiDHPZQDzHu2GL+aXqabhLUgFONcC8RIsbcVRPm+hQ+C9m9eBCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4b537541a0a6297c874a23f3ab3df7d1d41ca354c03290358f4de3b254a37cbb","last_reissued_at":"2026-07-05T04:35:00.745006Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:35:00.745006Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning neural state-space models: do we need a state estimator?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Dario Piga, Manas Mejari, Marco Forgione","submitted_at":"2022-06-26T17:15:35Z","abstract_excerpt":"In recent years, several algorithms for system identification with neural state-space models have been introduced. Most of the proposed approaches are aimed at reducing the computational complexity of the learning problem, by splitting the optimization over short sub-sequences extracted from a longer training dataset. Different sequences are then processed simultaneously within a minibatch, taking advantage of modern parallel hardware for deep learning. An issue arising in these methods is the need to assign an initial state for each of the sub-sequences, which is required to run simulations a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.12928","kind":"arxiv","version":1},"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/2206.12928/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":"2206.12928","created_at":"2026-07-05T04:35:00.745062+00:00"},{"alias_kind":"arxiv_version","alias_value":"2206.12928v1","created_at":"2026-07-05T04:35:00.745062+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.12928","created_at":"2026-07-05T04:35:00.745062+00:00"},{"alias_kind":"pith_short_12","alias_value":"JNJXKQNAUYUX","created_at":"2026-07-05T04:35:00.745062+00:00"},{"alias_kind":"pith_short_16","alias_value":"JNJXKQNAUYUXZB2K","created_at":"2026-07-05T04:35:00.745062+00:00"},{"alias_kind":"pith_short_8","alias_value":"JNJXKQNA","created_at":"2026-07-05T04:35:00.745062+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.24210","citing_title":"Graph Neural Ordinary Differential Equations for Power System Identification","ref_index":21,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JNJXKQNAUYUXZB2KEPZ2WPPX2H","json":"https://pith.science/pith/JNJXKQNAUYUXZB2KEPZ2WPPX2H.json","graph_json":"https://pith.science/api/pith-number/JNJXKQNAUYUXZB2KEPZ2WPPX2H/graph.json","events_json":"https://pith.science/api/pith-number/JNJXKQNAUYUXZB2KEPZ2WPPX2H/events.json","paper":"https://pith.science/paper/JNJXKQNA"},"agent_actions":{"view_html":"https://pith.science/pith/JNJXKQNAUYUXZB2KEPZ2WPPX2H","download_json":"https://pith.science/pith/JNJXKQNAUYUXZB2KEPZ2WPPX2H.json","view_paper":"https://pith.science/paper/JNJXKQNA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2206.12928&json=true","fetch_graph":"https://pith.science/api/pith-number/JNJXKQNAUYUXZB2KEPZ2WPPX2H/graph.json","fetch_events":"https://pith.science/api/pith-number/JNJXKQNAUYUXZB2KEPZ2WPPX2H/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JNJXKQNAUYUXZB2KEPZ2WPPX2H/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JNJXKQNAUYUXZB2KEPZ2WPPX2H/action/storage_attestation","attest_author":"https://pith.science/pith/JNJXKQNAUYUXZB2KEPZ2WPPX2H/action/author_attestation","sign_citation":"https://pith.science/pith/JNJXKQNAUYUXZB2KEPZ2WPPX2H/action/citation_signature","submit_replication":"https://pith.science/pith/JNJXKQNAUYUXZB2KEPZ2WPPX2H/action/replication_record"}},"created_at":"2026-07-05T04:35:00.745062+00:00","updated_at":"2026-07-05T04:35:00.745062+00:00"}