{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:7SSJXIYRTUYV44LZY5LO74LA2P","short_pith_number":"pith:7SSJXIYR","canonical_record":{"source":{"id":"1611.09630","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-11-29T13:49:31Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"3557acbd7a10d52295f0dca2a6742af0313966c1205687c309eaba7438ac50cc","abstract_canon_sha256":"fa628d55bb181c5be4b9608c1e5ae277bf8a38fe07b66a95f7b194519e1e94da"},"schema_version":"1.0"},"canonical_sha256":"fca49ba3119d315e7179c756eff160d3ddeea1b267885f054e1bf51545d30c0b","source":{"kind":"arxiv","id":"1611.09630","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.09630","created_at":"2026-05-18T00:51:59Z"},{"alias_kind":"arxiv_version","alias_value":"1611.09630v4","created_at":"2026-05-18T00:51:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.09630","created_at":"2026-05-18T00:51:59Z"},{"alias_kind":"pith_short_12","alias_value":"7SSJXIYRTUYV","created_at":"2026-05-18T12:30:04Z"},{"alias_kind":"pith_short_16","alias_value":"7SSJXIYRTUYV44LZ","created_at":"2026-05-18T12:30:04Z"},{"alias_kind":"pith_short_8","alias_value":"7SSJXIYR","created_at":"2026-05-18T12:30:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:7SSJXIYRTUYV44LZY5LO74LA2P","target":"record","payload":{"canonical_record":{"source":{"id":"1611.09630","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-11-29T13:49:31Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"3557acbd7a10d52295f0dca2a6742af0313966c1205687c309eaba7438ac50cc","abstract_canon_sha256":"fa628d55bb181c5be4b9608c1e5ae277bf8a38fe07b66a95f7b194519e1e94da"},"schema_version":"1.0"},"canonical_sha256":"fca49ba3119d315e7179c756eff160d3ddeea1b267885f054e1bf51545d30c0b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:51:59.734574Z","signature_b64":"Zrdsn1wjQ8cnS9CnmSRJwMAofoKqW1zKFkDz8jBIic34c4OfWpvTPn2aepk/Bi2yLWh4li8SF/jMoF+5AXT5AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fca49ba3119d315e7179c756eff160d3ddeea1b267885f054e1bf51545d30c0b","last_reissued_at":"2026-05-18T00:51:59.734035Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:51:59.734035Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1611.09630","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":"2026-05-18T00:51:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"O6V5sXgMP1xgMq+hrXidc5lVAXF04KxzNFQ0XRYM2l9KtpXHrnKRq94Mflwo/BHLAOaRSqldSjaNRTak77XjAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T19:03:41.626819Z"},"content_sha256":"799245912bb7996613afeac1b6eb7b4a739b3a20e9ebd34c9c435734df984119","schema_version":"1.0","event_id":"sha256:799245912bb7996613afeac1b6eb7b4a739b3a20e9ebd34c9c435734df984119"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:7SSJXIYRTUYV44LZY5LO74LA2P","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Improving Variational Auto-Encoders using Householder Flow","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Jakub M. Tomczak, Max Welling","submitted_at":"2016-11-29T13:49:31Z","abstract_excerpt":"Variational auto-encoders (VAE) are scalable and powerful generative models. However, the choice of the variational posterior determines tractability and flexibility of the VAE. Commonly, latent variables are modeled using the normal distribution with a diagonal covariance matrix. This results in computational efficiency but typically it is not flexible enough to match the true posterior distribution. One fashion of enriching the variational posterior distribution is application of normalizing flows, i.e., a series of invertible transformations to latent variables with a simple posterior. In t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.09630","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":""},"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":"2026-05-18T00:51:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tVNHB20QKE+ZDjHy8OsY+UpQeBuEEfuZ/CoasfsVD5mdggju487FStecsNDsthvYOiwhIAfRliPapwlTZWnsBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T19:03:41.627544Z"},"content_sha256":"63813223a8a31a8bf9990967fdb6986c8cb9e794a21e317bb0d0a81372915ec0","schema_version":"1.0","event_id":"sha256:63813223a8a31a8bf9990967fdb6986c8cb9e794a21e317bb0d0a81372915ec0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7SSJXIYRTUYV44LZY5LO74LA2P/bundle.json","state_url":"https://pith.science/pith/7SSJXIYRTUYV44LZY5LO74LA2P/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7SSJXIYRTUYV44LZY5LO74LA2P/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":"2026-06-08T19:03:41Z","links":{"resolver":"https://pith.science/pith/7SSJXIYRTUYV44LZY5LO74LA2P","bundle":"https://pith.science/pith/7SSJXIYRTUYV44LZY5LO74LA2P/bundle.json","state":"https://pith.science/pith/7SSJXIYRTUYV44LZY5LO74LA2P/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7SSJXIYRTUYV44LZY5LO74LA2P/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:7SSJXIYRTUYV44LZY5LO74LA2P","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":"fa628d55bb181c5be4b9608c1e5ae277bf8a38fe07b66a95f7b194519e1e94da","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-11-29T13:49:31Z","title_canon_sha256":"3557acbd7a10d52295f0dca2a6742af0313966c1205687c309eaba7438ac50cc"},"schema_version":"1.0","source":{"id":"1611.09630","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.09630","created_at":"2026-05-18T00:51:59Z"},{"alias_kind":"arxiv_version","alias_value":"1611.09630v4","created_at":"2026-05-18T00:51:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.09630","created_at":"2026-05-18T00:51:59Z"},{"alias_kind":"pith_short_12","alias_value":"7SSJXIYRTUYV","created_at":"2026-05-18T12:30:04Z"},{"alias_kind":"pith_short_16","alias_value":"7SSJXIYRTUYV44LZ","created_at":"2026-05-18T12:30:04Z"},{"alias_kind":"pith_short_8","alias_value":"7SSJXIYR","created_at":"2026-05-18T12:30:04Z"}],"graph_snapshots":[{"event_id":"sha256:63813223a8a31a8bf9990967fdb6986c8cb9e794a21e317bb0d0a81372915ec0","target":"graph","created_at":"2026-05-18T00:51:59Z","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":"Variational auto-encoders (VAE) are scalable and powerful generative models. However, the choice of the variational posterior determines tractability and flexibility of the VAE. Commonly, latent variables are modeled using the normal distribution with a diagonal covariance matrix. This results in computational efficiency but typically it is not flexible enough to match the true posterior distribution. One fashion of enriching the variational posterior distribution is application of normalizing flows, i.e., a series of invertible transformations to latent variables with a simple posterior. In t","authors_text":"Jakub M. Tomczak, Max Welling","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-11-29T13:49:31Z","title":"Improving Variational Auto-Encoders using Householder Flow"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.09630","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":"sha256:799245912bb7996613afeac1b6eb7b4a739b3a20e9ebd34c9c435734df984119","target":"record","created_at":"2026-05-18T00:51:59Z","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":"fa628d55bb181c5be4b9608c1e5ae277bf8a38fe07b66a95f7b194519e1e94da","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-11-29T13:49:31Z","title_canon_sha256":"3557acbd7a10d52295f0dca2a6742af0313966c1205687c309eaba7438ac50cc"},"schema_version":"1.0","source":{"id":"1611.09630","kind":"arxiv","version":4}},"canonical_sha256":"fca49ba3119d315e7179c756eff160d3ddeea1b267885f054e1bf51545d30c0b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fca49ba3119d315e7179c756eff160d3ddeea1b267885f054e1bf51545d30c0b","first_computed_at":"2026-05-18T00:51:59.734035Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:51:59.734035Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Zrdsn1wjQ8cnS9CnmSRJwMAofoKqW1zKFkDz8jBIic34c4OfWpvTPn2aepk/Bi2yLWh4li8SF/jMoF+5AXT5AQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:51:59.734574Z","signed_message":"canonical_sha256_bytes"},"source_id":"1611.09630","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:799245912bb7996613afeac1b6eb7b4a739b3a20e9ebd34c9c435734df984119","sha256:63813223a8a31a8bf9990967fdb6986c8cb9e794a21e317bb0d0a81372915ec0"],"state_sha256":"53f2eca3009dd2b960c282cccdb45906d9a4aa550a4297024fff656f01ffc98e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"j2dXSm0x50D2HmeC4dx2j71+mRdv1rWeMHEIQkkKidSCR+ykMBlroSjyUExhX7i3Of56LYaCNBjstcgPPP1yDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T19:03:41.631234Z","bundle_sha256":"a062001f018a4bb2cce7ecfb529f5e9786ee6ab271d8f76b5cf3b8c2d6471d7d"}}