{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:D7SNEWM7BVBV54QRS4O47ALCPE","short_pith_number":"pith:D7SNEWM7","canonical_record":{"source":{"id":"1702.08658","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-02-28T06:04:23Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"425c4bc5a570cae2f9135540ec507572a5beb37e7de68af93f139608d9f84ba0","abstract_canon_sha256":"b7dcf592092f4f1ca995098ace51e8c82bcd1b88f01812a0124775c545832a66"},"schema_version":"1.0"},"canonical_sha256":"1fe4d2599f0d435ef211971dcf8162793522ae5356cf5f7b58d1663cf989df67","source":{"kind":"arxiv","id":"1702.08658","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1702.08658","created_at":"2026-05-18T00:49:48Z"},{"alias_kind":"arxiv_version","alias_value":"1702.08658v1","created_at":"2026-05-18T00:49:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.08658","created_at":"2026-05-18T00:49:48Z"},{"alias_kind":"pith_short_12","alias_value":"D7SNEWM7BVBV","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_16","alias_value":"D7SNEWM7BVBV54QR","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_8","alias_value":"D7SNEWM7","created_at":"2026-05-18T12:31:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:D7SNEWM7BVBV54QRS4O47ALCPE","target":"record","payload":{"canonical_record":{"source":{"id":"1702.08658","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-02-28T06:04:23Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"425c4bc5a570cae2f9135540ec507572a5beb37e7de68af93f139608d9f84ba0","abstract_canon_sha256":"b7dcf592092f4f1ca995098ace51e8c82bcd1b88f01812a0124775c545832a66"},"schema_version":"1.0"},"canonical_sha256":"1fe4d2599f0d435ef211971dcf8162793522ae5356cf5f7b58d1663cf989df67","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:49:48.171217Z","signature_b64":"x8nbKJfbZvU6yD3WI4lzn+uP9lIJ0O97tN9NDw8uhoMMWmkW30vPXtznq2XwzU+cCHX4vDsPJ30YUvbXiHGnAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1fe4d2599f0d435ef211971dcf8162793522ae5356cf5f7b58d1663cf989df67","last_reissued_at":"2026-05-18T00:49:48.170692Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:49:48.170692Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1702.08658","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":"2026-05-18T00:49:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rZXfL4WVo1TYmsQKZZTxBEkzhK7iYeoTuiCzjK0Arg0IoleHwwI6wpFN9nciRTQW0W3f26GMbAkbS96NZdPtDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T03:53:29.558959Z"},"content_sha256":"711a0bc4e9eda6ceae696cd8565ee8d59f4e1e172ba93a4e6e80d861ebc0d7a2","schema_version":"1.0","event_id":"sha256:711a0bc4e9eda6ceae696cd8565ee8d59f4e1e172ba93a4e6e80d861ebc0d7a2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:D7SNEWM7BVBV54QRS4O47ALCPE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Towards Deeper Understanding of Variational Autoencoding Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Jiaming Song, Shengjia Zhao, Stefano Ermon","submitted_at":"2017-02-28T06:04:23Z","abstract_excerpt":"We propose a new family of optimization criteria for variational auto-encoding models, generalizing the standard evidence lower bound. We provide conditions under which they recover the data distribution and learn latent features, and formally show that common issues such as blurry samples and uninformative latent features arise when these conditions are not met. Based on these new insights, we propose a new sequential VAE model that can generate sharp samples on the LSUN image dataset based on pixel-wise reconstruction loss, and propose an optimization criterion that encourages unsupervised l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.08658","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":"2026-05-18T00:49:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UKbQ5fN0sNXaADMrXaMrAQZpAcBShG/V7tsRHnx9flTCKdj0gFsx4lSes7iElE4L4o5GqM+GBfVYgsTEdY4ZAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T03:53:29.559663Z"},"content_sha256":"75dd925d4b6edc758f97be94c67ad59d975ab38f494371ec9d5bd087c9254c24","schema_version":"1.0","event_id":"sha256:75dd925d4b6edc758f97be94c67ad59d975ab38f494371ec9d5bd087c9254c24"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/D7SNEWM7BVBV54QRS4O47ALCPE/bundle.json","state_url":"https://pith.science/pith/D7SNEWM7BVBV54QRS4O47ALCPE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/D7SNEWM7BVBV54QRS4O47ALCPE/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":"2026-05-27T03:53:29Z","links":{"resolver":"https://pith.science/pith/D7SNEWM7BVBV54QRS4O47ALCPE","bundle":"https://pith.science/pith/D7SNEWM7BVBV54QRS4O47ALCPE/bundle.json","state":"https://pith.science/pith/D7SNEWM7BVBV54QRS4O47ALCPE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/D7SNEWM7BVBV54QRS4O47ALCPE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:D7SNEWM7BVBV54QRS4O47ALCPE","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":"b7dcf592092f4f1ca995098ace51e8c82bcd1b88f01812a0124775c545832a66","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-02-28T06:04:23Z","title_canon_sha256":"425c4bc5a570cae2f9135540ec507572a5beb37e7de68af93f139608d9f84ba0"},"schema_version":"1.0","source":{"id":"1702.08658","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1702.08658","created_at":"2026-05-18T00:49:48Z"},{"alias_kind":"arxiv_version","alias_value":"1702.08658v1","created_at":"2026-05-18T00:49:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.08658","created_at":"2026-05-18T00:49:48Z"},{"alias_kind":"pith_short_12","alias_value":"D7SNEWM7BVBV","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_16","alias_value":"D7SNEWM7BVBV54QR","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_8","alias_value":"D7SNEWM7","created_at":"2026-05-18T12:31:10Z"}],"graph_snapshots":[{"event_id":"sha256:75dd925d4b6edc758f97be94c67ad59d975ab38f494371ec9d5bd087c9254c24","target":"graph","created_at":"2026-05-18T00:49:48Z","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":"We propose a new family of optimization criteria for variational auto-encoding models, generalizing the standard evidence lower bound. We provide conditions under which they recover the data distribution and learn latent features, and formally show that common issues such as blurry samples and uninformative latent features arise when these conditions are not met. Based on these new insights, we propose a new sequential VAE model that can generate sharp samples on the LSUN image dataset based on pixel-wise reconstruction loss, and propose an optimization criterion that encourages unsupervised l","authors_text":"Jiaming Song, Shengjia Zhao, Stefano Ermon","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-02-28T06:04:23Z","title":"Towards Deeper Understanding of Variational Autoencoding Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.08658","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":"sha256:711a0bc4e9eda6ceae696cd8565ee8d59f4e1e172ba93a4e6e80d861ebc0d7a2","target":"record","created_at":"2026-05-18T00:49:48Z","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":"b7dcf592092f4f1ca995098ace51e8c82bcd1b88f01812a0124775c545832a66","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-02-28T06:04:23Z","title_canon_sha256":"425c4bc5a570cae2f9135540ec507572a5beb37e7de68af93f139608d9f84ba0"},"schema_version":"1.0","source":{"id":"1702.08658","kind":"arxiv","version":1}},"canonical_sha256":"1fe4d2599f0d435ef211971dcf8162793522ae5356cf5f7b58d1663cf989df67","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1fe4d2599f0d435ef211971dcf8162793522ae5356cf5f7b58d1663cf989df67","first_computed_at":"2026-05-18T00:49:48.170692Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:49:48.170692Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"x8nbKJfbZvU6yD3WI4lzn+uP9lIJ0O97tN9NDw8uhoMMWmkW30vPXtznq2XwzU+cCHX4vDsPJ30YUvbXiHGnAg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:49:48.171217Z","signed_message":"canonical_sha256_bytes"},"source_id":"1702.08658","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:711a0bc4e9eda6ceae696cd8565ee8d59f4e1e172ba93a4e6e80d861ebc0d7a2","sha256:75dd925d4b6edc758f97be94c67ad59d975ab38f494371ec9d5bd087c9254c24"],"state_sha256":"9481658213ccee0e0f9618b40f0a372949954027bb6f537a21dc5ce91786b690"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"niHPPr+tB6u5t5yY23FUxCNEgUMRsXtEUwQCU+it62qqh5pf70P9/Mtl3MeeZWJVdwrp7nTPiT4TEfhLIImPDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T03:53:29.563226Z","bundle_sha256":"253a09fe855488747b44210d0752a99983e19ee6a9f53954b7e4ae060a8ecd98"}}