{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:56RRKZHIVBDIGWGINNCRNETJNY","short_pith_number":"pith:56RRKZHI","canonical_record":{"source":{"id":"2002.02913","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-02-07T17:27:30Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"af95a231ffaf8f34ec7bf13c0f1aaa37727d5fe60739ff283f4f22a8a9ca3006","abstract_canon_sha256":"058b4b717950d6ed048288f9d293ffdf9e4c8bb71330dbc61d5a5f03299ce180"},"schema_version":"1.0"},"canonical_sha256":"efa31564e8a8468358c86b451692696e07cebd017c9d178277974372b4dc8647","source":{"kind":"arxiv","id":"2002.02913","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2002.02913","created_at":"2026-07-05T01:13:43Z"},{"alias_kind":"arxiv_version","alias_value":"2002.02913v4","created_at":"2026-07-05T01:13:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.02913","created_at":"2026-07-05T01:13:43Z"},{"alias_kind":"pith_short_12","alias_value":"56RRKZHIVBDI","created_at":"2026-07-05T01:13:43Z"},{"alias_kind":"pith_short_16","alias_value":"56RRKZHIVBDIGWGI","created_at":"2026-07-05T01:13:43Z"},{"alias_kind":"pith_short_8","alias_value":"56RRKZHI","created_at":"2026-07-05T01:13:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:56RRKZHIVBDIGWGINNCRNETJNY","target":"record","payload":{"canonical_record":{"source":{"id":"2002.02913","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-02-07T17:27:30Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"af95a231ffaf8f34ec7bf13c0f1aaa37727d5fe60739ff283f4f22a8a9ca3006","abstract_canon_sha256":"058b4b717950d6ed048288f9d293ffdf9e4c8bb71330dbc61d5a5f03299ce180"},"schema_version":"1.0"},"canonical_sha256":"efa31564e8a8468358c86b451692696e07cebd017c9d178277974372b4dc8647","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:13:43.276131Z","signature_b64":"VzkLnc1U/ggEJmXRHjRwWQQn6aa+UcRJjh33AV8quykzhTAclFWd3l1ZVtE8qPdMArrELjAuZkVWIoziHopKDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"efa31564e8a8468358c86b451692696e07cebd017c9d178277974372b4dc8647","last_reissued_at":"2026-07-05T01:13:43.275594Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:13:43.275594Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2002.02913","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:13:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LcTopmRXuTftIEL1BL487iZQP1qns/+vukdjGt05qbQqD6hOpMtpBwXZDunHqOygDBnEGvbUGcp1ExUZlwFLDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T21:56:31.999349Z"},"content_sha256":"b1d826ff71190696a7db1ba52d34fe2c60aa92f4ba011419fa1c1c432f90dfd8","schema_version":"1.0","event_id":"sha256:b1d826ff71190696a7db1ba52d34fe2c60aa92f4ba011419fa1c1c432f90dfd8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:56RRKZHIVBDIGWGINNCRNETJNY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Autoencoders with Relational Regularization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Dixin Luo, Hongteng Xu, Lawrence Carin, Ricardo Henao, Svati Shah","submitted_at":"2020-02-07T17:27:30Z","abstract_excerpt":"A new algorithmic framework is proposed for learning autoencoders of data distributions. We minimize the discrepancy between the model and target distributions, with a \\emph{relational regularization} on the learnable latent prior. This regularization penalizes the fused Gromov-Wasserstein (FGW) distance between the latent prior and its corresponding posterior, allowing one to flexibly learn a structured prior distribution associated with the generative model. Moreover, it helps co-training of multiple autoencoders even if they have heterogeneous architectures and incomparable latent spaces. W"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.02913","kind":"arxiv","version":4},"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/2002.02913/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:13:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3/YCkIHfJPZMRP7DK2I6D7BcCitMrBkX53dFqZYzuxAxPI4ebUMs0PCdmWpN2mYuEQggVI9jzjpb3puPbPiQCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T21:56:31.999842Z"},"content_sha256":"9b4a6673aec10b18d0dbbb438f82d1b03e8e29bc60bfa0a7b8eae805ebf0eb6a","schema_version":"1.0","event_id":"sha256:9b4a6673aec10b18d0dbbb438f82d1b03e8e29bc60bfa0a7b8eae805ebf0eb6a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/56RRKZHIVBDIGWGINNCRNETJNY/bundle.json","state_url":"https://pith.science/pith/56RRKZHIVBDIGWGINNCRNETJNY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/56RRKZHIVBDIGWGINNCRNETJNY/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":"2026-08-08T21:56:32Z","links":{"resolver":"https://pith.science/pith/56RRKZHIVBDIGWGINNCRNETJNY","bundle":"https://pith.science/pith/56RRKZHIVBDIGWGINNCRNETJNY/bundle.json","state":"https://pith.science/pith/56RRKZHIVBDIGWGINNCRNETJNY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/56RRKZHIVBDIGWGINNCRNETJNY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:56RRKZHIVBDIGWGINNCRNETJNY","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":"058b4b717950d6ed048288f9d293ffdf9e4c8bb71330dbc61d5a5f03299ce180","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-02-07T17:27:30Z","title_canon_sha256":"af95a231ffaf8f34ec7bf13c0f1aaa37727d5fe60739ff283f4f22a8a9ca3006"},"schema_version":"1.0","source":{"id":"2002.02913","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2002.02913","created_at":"2026-07-05T01:13:43Z"},{"alias_kind":"arxiv_version","alias_value":"2002.02913v4","created_at":"2026-07-05T01:13:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.02913","created_at":"2026-07-05T01:13:43Z"},{"alias_kind":"pith_short_12","alias_value":"56RRKZHIVBDI","created_at":"2026-07-05T01:13:43Z"},{"alias_kind":"pith_short_16","alias_value":"56RRKZHIVBDIGWGI","created_at":"2026-07-05T01:13:43Z"},{"alias_kind":"pith_short_8","alias_value":"56RRKZHI","created_at":"2026-07-05T01:13:43Z"}],"graph_snapshots":[{"event_id":"sha256:9b4a6673aec10b18d0dbbb438f82d1b03e8e29bc60bfa0a7b8eae805ebf0eb6a","target":"graph","created_at":"2026-07-05T01:13:43Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2002.02913/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"A new algorithmic framework is proposed for learning autoencoders of data distributions. We minimize the discrepancy between the model and target distributions, with a \\emph{relational regularization} on the learnable latent prior. This regularization penalizes the fused Gromov-Wasserstein (FGW) distance between the latent prior and its corresponding posterior, allowing one to flexibly learn a structured prior distribution associated with the generative model. Moreover, it helps co-training of multiple autoencoders even if they have heterogeneous architectures and incomparable latent spaces. W","authors_text":"Dixin Luo, Hongteng Xu, Lawrence Carin, Ricardo Henao, Svati Shah","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-02-07T17:27:30Z","title":"Learning Autoencoders with Relational Regularization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.02913","kind":"arxiv","version":4},"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:b1d826ff71190696a7db1ba52d34fe2c60aa92f4ba011419fa1c1c432f90dfd8","target":"record","created_at":"2026-07-05T01:13:43Z","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":"058b4b717950d6ed048288f9d293ffdf9e4c8bb71330dbc61d5a5f03299ce180","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-02-07T17:27:30Z","title_canon_sha256":"af95a231ffaf8f34ec7bf13c0f1aaa37727d5fe60739ff283f4f22a8a9ca3006"},"schema_version":"1.0","source":{"id":"2002.02913","kind":"arxiv","version":4}},"canonical_sha256":"efa31564e8a8468358c86b451692696e07cebd017c9d178277974372b4dc8647","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"efa31564e8a8468358c86b451692696e07cebd017c9d178277974372b4dc8647","first_computed_at":"2026-07-05T01:13:43.275594Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:13:43.275594Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VzkLnc1U/ggEJmXRHjRwWQQn6aa+UcRJjh33AV8quykzhTAclFWd3l1ZVtE8qPdMArrELjAuZkVWIoziHopKDg==","signature_status":"signed_v1","signed_at":"2026-07-05T01:13:43.276131Z","signed_message":"canonical_sha256_bytes"},"source_id":"2002.02913","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b1d826ff71190696a7db1ba52d34fe2c60aa92f4ba011419fa1c1c432f90dfd8","sha256:9b4a6673aec10b18d0dbbb438f82d1b03e8e29bc60bfa0a7b8eae805ebf0eb6a"],"state_sha256":"286a2847887ba8211687ec78bc9df9512f220f6210782d61cd6b571e87771d8e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3/GG9SG3poox8eRYR0iz0hJ33G+4PKhqTtZ639S/OrrRh6bKqZVrJ6aWOzsg03hl41AcSbnZuC/mGkew7CGFAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T21:56:32.004444Z","bundle_sha256":"56bbc7db6a7b089eba3546fbe7da6461dd8c09dbc35b7b8729bc03190671232d"}}