{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:HADYW6VRHP4F2452CNWBCHT7L7","short_pith_number":"pith:HADYW6VR","canonical_record":{"source":{"id":"1605.09080","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-05-30T00:32:11Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"ba05e1f05ae4ebf998da3846c68f6111218ec57add05def0127e3348eec2320a","abstract_canon_sha256":"22f0b309edb3f0c8fa0c27e5344ecd9620de5e3c6221650535de5b062564451d"},"schema_version":"1.0"},"canonical_sha256":"38078b7ab13bf85d73ba136c111e7f5ff597879a077f96dedab74bc4ef721eb3","source":{"kind":"arxiv","id":"1605.09080","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1605.09080","created_at":"2026-05-18T00:59:24Z"},{"alias_kind":"arxiv_version","alias_value":"1605.09080v5","created_at":"2026-05-18T00:59:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1605.09080","created_at":"2026-05-18T00:59:24Z"},{"alias_kind":"pith_short_12","alias_value":"HADYW6VRHP4F","created_at":"2026-05-18T12:30:19Z"},{"alias_kind":"pith_short_16","alias_value":"HADYW6VRHP4F2452","created_at":"2026-05-18T12:30:19Z"},{"alias_kind":"pith_short_8","alias_value":"HADYW6VR","created_at":"2026-05-18T12:30:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:HADYW6VRHP4F2452CNWBCHT7L7","target":"record","payload":{"canonical_record":{"source":{"id":"1605.09080","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-05-30T00:32:11Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"ba05e1f05ae4ebf998da3846c68f6111218ec57add05def0127e3348eec2320a","abstract_canon_sha256":"22f0b309edb3f0c8fa0c27e5344ecd9620de5e3c6221650535de5b062564451d"},"schema_version":"1.0"},"canonical_sha256":"38078b7ab13bf85d73ba136c111e7f5ff597879a077f96dedab74bc4ef721eb3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:59:24.975244Z","signature_b64":"azp6HD1JEnlFiSjgQ8T7xaKLdPl/lgkBShqpSljLvyAO9xuIfAb0WnDxXypcj2cNx3UpM891ZQek522hH9AxBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"38078b7ab13bf85d73ba136c111e7f5ff597879a077f96dedab74bc4ef721eb3","last_reissued_at":"2026-05-18T00:59:24.974493Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:59:24.974493Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1605.09080","source_version":5,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:59:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZM3Ft/zmFe1RaVHbyaPmswJl8rvg6SMbwokk4AkPMMfb/bzoUD5CFHkci6qwfKwI0/G+Rc+XPz+XBNiIJrkIBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T04:54:53.852464Z"},"content_sha256":"b42c53c3e8e8427995d7b18b72e75d5ccd1bf49f7e7a0f4a3e9fdb7522ca4979","schema_version":"1.0","event_id":"sha256:b42c53c3e8e8427995d7b18b72e75d5ccd1bf49f7e7a0f4a3e9fdb7522ca4979"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:HADYW6VRHP4F2452CNWBCHT7L7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Spectral Methods for Correlated Topic Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Animashree Anandkumar, Forough Arabshahi","submitted_at":"2016-05-30T00:32:11Z","abstract_excerpt":"In this paper, we propose guaranteed spectral methods for learning a broad range of topic models, which generalize the popular Latent Dirichlet Allocation (LDA). We overcome the limitation of LDA to incorporate arbitrary topic correlations, by assuming that the hidden topic proportions are drawn from a flexible class of Normalized Infinitely Divisible (NID) distributions. NID distributions are generated through the process of normalizing a family of independent Infinitely Divisible (ID) random variables. The Dirichlet distribution is a special case obtained by normalizing a set of Gamma random"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1605.09080","kind":"arxiv","version":5},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:59:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7m50jpvpoDVtUP+P1FJx4EKug8tPi/90KnrbDbI6a/KGN4qf1VAC7cMxnsXJo9t78DyBgZXrGcong7+x0stsBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T04:54:53.852809Z"},"content_sha256":"2ecee449d2c512633b08c53edb387218c3369fc751baa40f0e11d02be82b8b73","schema_version":"1.0","event_id":"sha256:2ecee449d2c512633b08c53edb387218c3369fc751baa40f0e11d02be82b8b73"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HADYW6VRHP4F2452CNWBCHT7L7/bundle.json","state_url":"https://pith.science/pith/HADYW6VRHP4F2452CNWBCHT7L7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HADYW6VRHP4F2452CNWBCHT7L7/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-30T04:54:53Z","links":{"resolver":"https://pith.science/pith/HADYW6VRHP4F2452CNWBCHT7L7","bundle":"https://pith.science/pith/HADYW6VRHP4F2452CNWBCHT7L7/bundle.json","state":"https://pith.science/pith/HADYW6VRHP4F2452CNWBCHT7L7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HADYW6VRHP4F2452CNWBCHT7L7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:HADYW6VRHP4F2452CNWBCHT7L7","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":"22f0b309edb3f0c8fa0c27e5344ecd9620de5e3c6221650535de5b062564451d","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-05-30T00:32:11Z","title_canon_sha256":"ba05e1f05ae4ebf998da3846c68f6111218ec57add05def0127e3348eec2320a"},"schema_version":"1.0","source":{"id":"1605.09080","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1605.09080","created_at":"2026-05-18T00:59:24Z"},{"alias_kind":"arxiv_version","alias_value":"1605.09080v5","created_at":"2026-05-18T00:59:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1605.09080","created_at":"2026-05-18T00:59:24Z"},{"alias_kind":"pith_short_12","alias_value":"HADYW6VRHP4F","created_at":"2026-05-18T12:30:19Z"},{"alias_kind":"pith_short_16","alias_value":"HADYW6VRHP4F2452","created_at":"2026-05-18T12:30:19Z"},{"alias_kind":"pith_short_8","alias_value":"HADYW6VR","created_at":"2026-05-18T12:30:19Z"}],"graph_snapshots":[{"event_id":"sha256:2ecee449d2c512633b08c53edb387218c3369fc751baa40f0e11d02be82b8b73","target":"graph","created_at":"2026-05-18T00:59:24Z","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":"In this paper, we propose guaranteed spectral methods for learning a broad range of topic models, which generalize the popular Latent Dirichlet Allocation (LDA). We overcome the limitation of LDA to incorporate arbitrary topic correlations, by assuming that the hidden topic proportions are drawn from a flexible class of Normalized Infinitely Divisible (NID) distributions. NID distributions are generated through the process of normalizing a family of independent Infinitely Divisible (ID) random variables. The Dirichlet distribution is a special case obtained by normalizing a set of Gamma random","authors_text":"Animashree Anandkumar, Forough Arabshahi","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-05-30T00:32:11Z","title":"Spectral Methods for Correlated Topic Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1605.09080","kind":"arxiv","version":5},"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:b42c53c3e8e8427995d7b18b72e75d5ccd1bf49f7e7a0f4a3e9fdb7522ca4979","target":"record","created_at":"2026-05-18T00:59:24Z","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":"22f0b309edb3f0c8fa0c27e5344ecd9620de5e3c6221650535de5b062564451d","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-05-30T00:32:11Z","title_canon_sha256":"ba05e1f05ae4ebf998da3846c68f6111218ec57add05def0127e3348eec2320a"},"schema_version":"1.0","source":{"id":"1605.09080","kind":"arxiv","version":5}},"canonical_sha256":"38078b7ab13bf85d73ba136c111e7f5ff597879a077f96dedab74bc4ef721eb3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"38078b7ab13bf85d73ba136c111e7f5ff597879a077f96dedab74bc4ef721eb3","first_computed_at":"2026-05-18T00:59:24.974493Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:59:24.974493Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"azp6HD1JEnlFiSjgQ8T7xaKLdPl/lgkBShqpSljLvyAO9xuIfAb0WnDxXypcj2cNx3UpM891ZQek522hH9AxBA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:59:24.975244Z","signed_message":"canonical_sha256_bytes"},"source_id":"1605.09080","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b42c53c3e8e8427995d7b18b72e75d5ccd1bf49f7e7a0f4a3e9fdb7522ca4979","sha256:2ecee449d2c512633b08c53edb387218c3369fc751baa40f0e11d02be82b8b73"],"state_sha256":"4eaab19e641d50f324f65d3694423c4e275a2e0cf9dfbc2d58556e5c537251c8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lMTWVodKhd5zIpKbcmrSnNk5aJA5tq31g2/Lj6IR8SFkerukZ/spj+r1mZcZeGbfWjpQ5rWgaVsr46G1jfrVBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T04:54:53.855082Z","bundle_sha256":"988845165e7e2901b9e12e7f1964b88845426df5174a2e3063f340d29b10f728"}}