{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:4VI7SO2QUFLBJ2MDAOBD24K2XM","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"c25995d88d1d1eba68e78aa99e253faf355ad3513edb1bd4446a78298e193121","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-17T02:02:30Z","title_canon_sha256":"9a01ddcb8f4aecd0ba3c8a52bc3bb262e1db2cdee9768ba417b082e026eaee54"},"schema_version":"1.0","source":{"id":"1905.07088","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.07088","created_at":"2026-05-17T23:42:07Z"},{"alias_kind":"arxiv_version","alias_value":"1905.07088v2","created_at":"2026-05-17T23:42:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.07088","created_at":"2026-05-17T23:42:07Z"},{"alias_kind":"pith_short_12","alias_value":"4VI7SO2QUFLB","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"4VI7SO2QUFLBJ2MD","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"4VI7SO2Q","created_at":"2026-05-18T12:33:10Z"}],"graph_snapshots":[{"event_id":"sha256:2506247fa5785ebf21f917ea6070dadea7c2a74c0c7d15e0bac4089ec1d28fa1","target":"graph","created_at":"2026-05-17T23:42:07Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Score matching is a popular method for estimating unnormalized statistical models. However, it has been so far limited to simple, shallow models or low-dimensional data, due to the difficulty of computing the Hessian of log-density functions. We show this difficulty can be mitigated by projecting the scores onto random vectors before comparing them. This objective, called sliced score matching, only involves Hessian-vector products, which can be easily implemented using reverse-mode automatic differentiation. Therefore, sliced score matching is amenable to more complex models and higher dimens","authors_text":"Jiaxin Shi, Sahaj Garg, Stefano Ermon, Yang Song","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-17T02:02:30Z","title":"Sliced Score Matching: A Scalable Approach to Density and Score Estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.07088","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a8b2b24c09814b87b00e9b1f2674abde03f7a3ad273c375bab176060badceb0e","target":"record","created_at":"2026-05-17T23:42:07Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"c25995d88d1d1eba68e78aa99e253faf355ad3513edb1bd4446a78298e193121","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-17T02:02:30Z","title_canon_sha256":"9a01ddcb8f4aecd0ba3c8a52bc3bb262e1db2cdee9768ba417b082e026eaee54"},"schema_version":"1.0","source":{"id":"1905.07088","kind":"arxiv","version":2}},"canonical_sha256":"e551f93b50a15614e98303823d715abb1934232d11878b97afa6eac0254a759c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e551f93b50a15614e98303823d715abb1934232d11878b97afa6eac0254a759c","first_computed_at":"2026-05-17T23:42:07.930376Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:42:07.930376Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1yCIJ0qd3ytoD3NkAAUAvpmv4uHduYkCAiLFZF+8f0g9LK0O1psQtbDggy871lbVsJh97V1s4GFtespTitDICg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:42:07.930978Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.07088","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a8b2b24c09814b87b00e9b1f2674abde03f7a3ad273c375bab176060badceb0e","sha256:2506247fa5785ebf21f917ea6070dadea7c2a74c0c7d15e0bac4089ec1d28fa1"],"state_sha256":"e3bc8e5cebba46a2aa7f82f8da0615c48ddfd4557bd73dad147236cd72056c2c"}