{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:LYSD22YPHCVTTMTOQDOJNIQH3X","short_pith_number":"pith:LYSD22YP","schema_version":"1.0","canonical_sha256":"5e243d6b0f38ab39b26e80dc96a207dded98aabb01b4010c5142616b6b4631ba","source":{"kind":"arxiv","id":"1705.08982","version":1},"attestation_state":"computed","paper":{"title":"Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Hongyuan Zha, Junchi Yan, Shuai Xiao, Stephen M. Chu, Xiaokang Yang","submitted_at":"2017-05-24T22:23:14Z","abstract_excerpt":"Event sequence, asynchronously generated with random timestamp, is ubiquitous among applications. The precise and arbitrary timestamp can carry important clues about the underlying dynamics, and has lent the event data fundamentally different from the time-series whereby series is indexed with fixed and equal time interval. One expressive mathematical tool for modeling event is point process. The intensity functions of many point processes involve two components: the background and the effect by the history. Due to its inherent spontaneousness, the background can be treated as a time series wh"},"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":"1705.08982","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-05-24T22:23:14Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"56a46033a66b567d472399314410f076713b2e32c29984f6380abcbe4ddd41c6","abstract_canon_sha256":"b8e58447c8ed30ec5ba8b273c286354bfc7efc25a313f21c5fbd68282ce0981a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:43:40.763790Z","signature_b64":"k5MQ9zI7bTbGtmJ82Peu345dH7+NFpQzApsQw93PHcgPmQ7aT6+vBYuaEgHwIuGroGAYjPnGNM7afPvynpqtDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5e243d6b0f38ab39b26e80dc96a207dded98aabb01b4010c5142616b6b4631ba","last_reissued_at":"2026-05-18T00:43:40.763191Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:43:40.763191Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Hongyuan Zha, Junchi Yan, Shuai Xiao, Stephen M. Chu, Xiaokang Yang","submitted_at":"2017-05-24T22:23:14Z","abstract_excerpt":"Event sequence, asynchronously generated with random timestamp, is ubiquitous among applications. The precise and arbitrary timestamp can carry important clues about the underlying dynamics, and has lent the event data fundamentally different from the time-series whereby series is indexed with fixed and equal time interval. One expressive mathematical tool for modeling event is point process. The intensity functions of many point processes involve two components: the background and the effect by the history. Due to its inherent spontaneousness, the background can be treated as a time series wh"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.08982","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":""},"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":"1705.08982","created_at":"2026-05-18T00:43:40.763265+00:00"},{"alias_kind":"arxiv_version","alias_value":"1705.08982v1","created_at":"2026-05-18T00:43:40.763265+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.08982","created_at":"2026-05-18T00:43:40.763265+00:00"},{"alias_kind":"pith_short_12","alias_value":"LYSD22YPHCVT","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_16","alias_value":"LYSD22YPHCVTTMTO","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_8","alias_value":"LYSD22YP","created_at":"2026-05-18T12:31:28.150371+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LYSD22YPHCVTTMTOQDOJNIQH3X","json":"https://pith.science/pith/LYSD22YPHCVTTMTOQDOJNIQH3X.json","graph_json":"https://pith.science/api/pith-number/LYSD22YPHCVTTMTOQDOJNIQH3X/graph.json","events_json":"https://pith.science/api/pith-number/LYSD22YPHCVTTMTOQDOJNIQH3X/events.json","paper":"https://pith.science/paper/LYSD22YP"},"agent_actions":{"view_html":"https://pith.science/pith/LYSD22YPHCVTTMTOQDOJNIQH3X","download_json":"https://pith.science/pith/LYSD22YPHCVTTMTOQDOJNIQH3X.json","view_paper":"https://pith.science/paper/LYSD22YP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1705.08982&json=true","fetch_graph":"https://pith.science/api/pith-number/LYSD22YPHCVTTMTOQDOJNIQH3X/graph.json","fetch_events":"https://pith.science/api/pith-number/LYSD22YPHCVTTMTOQDOJNIQH3X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LYSD22YPHCVTTMTOQDOJNIQH3X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LYSD22YPHCVTTMTOQDOJNIQH3X/action/storage_attestation","attest_author":"https://pith.science/pith/LYSD22YPHCVTTMTOQDOJNIQH3X/action/author_attestation","sign_citation":"https://pith.science/pith/LYSD22YPHCVTTMTOQDOJNIQH3X/action/citation_signature","submit_replication":"https://pith.science/pith/LYSD22YPHCVTTMTOQDOJNIQH3X/action/replication_record"}},"created_at":"2026-05-18T00:43:40.763265+00:00","updated_at":"2026-05-18T00:43:40.763265+00:00"}