{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:XCY7KYB565GGPI7TUL2GUXMWBA","short_pith_number":"pith:XCY7KYB5","canonical_record":{"source":{"id":"1709.05612","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-09-17T06:21:40Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"73d83ad40bb664d468354a37e2c5b169e1f0ccf3d34d6296d5d28d42597a5b05","abstract_canon_sha256":"2a515ad6cbda59e47e04546b0580a10678eb1210aa45a60eec8fc64fee5f301e"},"schema_version":"1.0"},"canonical_sha256":"b8b1f5603df74c67a3f3a2f46a5d96082abb6984585542fd9ee63d52b0fa44fe","source":{"kind":"arxiv","id":"1709.05612","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.05612","created_at":"2026-05-18T00:35:00Z"},{"alias_kind":"arxiv_version","alias_value":"1709.05612v1","created_at":"2026-05-18T00:35:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.05612","created_at":"2026-05-18T00:35:00Z"},{"alias_kind":"pith_short_12","alias_value":"XCY7KYB565GG","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_16","alias_value":"XCY7KYB565GGPI7T","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_8","alias_value":"XCY7KYB5","created_at":"2026-05-18T12:31:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:XCY7KYB565GGPI7TUL2GUXMWBA","target":"record","payload":{"canonical_record":{"source":{"id":"1709.05612","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-09-17T06:21:40Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"73d83ad40bb664d468354a37e2c5b169e1f0ccf3d34d6296d5d28d42597a5b05","abstract_canon_sha256":"2a515ad6cbda59e47e04546b0580a10678eb1210aa45a60eec8fc64fee5f301e"},"schema_version":"1.0"},"canonical_sha256":"b8b1f5603df74c67a3f3a2f46a5d96082abb6984585542fd9ee63d52b0fa44fe","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:35:00.131894Z","signature_b64":"dse9ynNvvJhMupby9GbWRecu3wzw04/LNKmuT0+Op57tG9j9q9yxLlxTAOa4Cm6tKjTJ6Gbk6+cTgLUVsVPAAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b8b1f5603df74c67a3f3a2f46a5d96082abb6984585542fd9ee63d52b0fa44fe","last_reissued_at":"2026-05-18T00:35:00.131151Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:35:00.131151Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1709.05612","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:35:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TrqZjq07iOKmGo0+kjgFl+S//ckr4uaQvViwdVMN+t9B0vZdpiQN+wjaoHiIBKAS6W5NuzBheWON7H0KZy0GDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T10:36:19.718542Z"},"content_sha256":"d0d0a011692ae6f77f92e2c66c54a20a50ffb1f80d1034cdd664fbb50d5ffdc6","schema_version":"1.0","event_id":"sha256:d0d0a011692ae6f77f92e2c66c54a20a50ffb1f80d1034cdd664fbb50d5ffdc6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:XCY7KYB565GGPI7TUL2GUXMWBA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multi-Entity Dependence Learning with Rich Context via Conditional Variational Auto-encoder","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Carla P. Gomes, Di Chen, Luming Tang, Yexiang Xue","submitted_at":"2017-09-17T06:21:40Z","abstract_excerpt":"Multi-Entity Dependence Learning (MEDL) explores conditional correlations among multiple entities. The availability of rich contextual information requires a nimble learning scheme that tightly integrates with deep neural networks and has the ability to capture correlation structures among exponentially many outcomes. We propose MEDL_CVAE, which encodes a conditional multivariate distribution as a generating process. As a result, the variational lower bound of the joint likelihood can be optimized via a conditional variational auto-encoder and trained end-to-end on GPUs. Our MEDL_CVAE was moti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.05612","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:35:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"G2C9v7FIVZ432yQ0xMC/ZURki4oQpyy25YS25Zqk+ZrUp5w3uicBWKOU7wvp9VRatTjbJw5onupYTxIGZsnYBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T10:36:19.719173Z"},"content_sha256":"dd2b537114d989617b580b06750277beb0a2bc6c16f8db94084e48ede1e0e35f","schema_version":"1.0","event_id":"sha256:dd2b537114d989617b580b06750277beb0a2bc6c16f8db94084e48ede1e0e35f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XCY7KYB565GGPI7TUL2GUXMWBA/bundle.json","state_url":"https://pith.science/pith/XCY7KYB565GGPI7TUL2GUXMWBA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XCY7KYB565GGPI7TUL2GUXMWBA/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":"2026-06-07T10:36:19Z","links":{"resolver":"https://pith.science/pith/XCY7KYB565GGPI7TUL2GUXMWBA","bundle":"https://pith.science/pith/XCY7KYB565GGPI7TUL2GUXMWBA/bundle.json","state":"https://pith.science/pith/XCY7KYB565GGPI7TUL2GUXMWBA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XCY7KYB565GGPI7TUL2GUXMWBA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:XCY7KYB565GGPI7TUL2GUXMWBA","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":"2a515ad6cbda59e47e04546b0580a10678eb1210aa45a60eec8fc64fee5f301e","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-09-17T06:21:40Z","title_canon_sha256":"73d83ad40bb664d468354a37e2c5b169e1f0ccf3d34d6296d5d28d42597a5b05"},"schema_version":"1.0","source":{"id":"1709.05612","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.05612","created_at":"2026-05-18T00:35:00Z"},{"alias_kind":"arxiv_version","alias_value":"1709.05612v1","created_at":"2026-05-18T00:35:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.05612","created_at":"2026-05-18T00:35:00Z"},{"alias_kind":"pith_short_12","alias_value":"XCY7KYB565GG","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_16","alias_value":"XCY7KYB565GGPI7T","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_8","alias_value":"XCY7KYB5","created_at":"2026-05-18T12:31:53Z"}],"graph_snapshots":[{"event_id":"sha256:dd2b537114d989617b580b06750277beb0a2bc6c16f8db94084e48ede1e0e35f","target":"graph","created_at":"2026-05-18T00:35:00Z","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":"Multi-Entity Dependence Learning (MEDL) explores conditional correlations among multiple entities. The availability of rich contextual information requires a nimble learning scheme that tightly integrates with deep neural networks and has the ability to capture correlation structures among exponentially many outcomes. We propose MEDL_CVAE, which encodes a conditional multivariate distribution as a generating process. As a result, the variational lower bound of the joint likelihood can be optimized via a conditional variational auto-encoder and trained end-to-end on GPUs. Our MEDL_CVAE was moti","authors_text":"Carla P. Gomes, Di Chen, Luming Tang, Yexiang Xue","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-09-17T06:21:40Z","title":"Multi-Entity Dependence Learning with Rich Context via Conditional Variational Auto-encoder"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.05612","kind":"arxiv","version":1},"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:d0d0a011692ae6f77f92e2c66c54a20a50ffb1f80d1034cdd664fbb50d5ffdc6","target":"record","created_at":"2026-05-18T00:35:00Z","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":"2a515ad6cbda59e47e04546b0580a10678eb1210aa45a60eec8fc64fee5f301e","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-09-17T06:21:40Z","title_canon_sha256":"73d83ad40bb664d468354a37e2c5b169e1f0ccf3d34d6296d5d28d42597a5b05"},"schema_version":"1.0","source":{"id":"1709.05612","kind":"arxiv","version":1}},"canonical_sha256":"b8b1f5603df74c67a3f3a2f46a5d96082abb6984585542fd9ee63d52b0fa44fe","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b8b1f5603df74c67a3f3a2f46a5d96082abb6984585542fd9ee63d52b0fa44fe","first_computed_at":"2026-05-18T00:35:00.131151Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:35:00.131151Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dse9ynNvvJhMupby9GbWRecu3wzw04/LNKmuT0+Op57tG9j9q9yxLlxTAOa4Cm6tKjTJ6Gbk6+cTgLUVsVPAAw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:35:00.131894Z","signed_message":"canonical_sha256_bytes"},"source_id":"1709.05612","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d0d0a011692ae6f77f92e2c66c54a20a50ffb1f80d1034cdd664fbb50d5ffdc6","sha256:dd2b537114d989617b580b06750277beb0a2bc6c16f8db94084e48ede1e0e35f"],"state_sha256":"bb4675c97853e5b2e7d61572f6580d18d4b2ec3aefa210e8b99f0938e824cf93"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"w6ibJ88frGC2P+9xD2D6MxGfLOZKiftJDYoEMXRWykE4vY1Xrxy3/ZGecihUQ6DuHJa+1hWwAGdH96hLfFCDDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T10:36:19.722640Z","bundle_sha256":"9d5fa81887b0232fbbcbb82f7dfe9820b140ac41f2c1e773430ffab521bc55d8"}}