{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:KDQFKFGLJL5AIGYH4352CD3NNC","short_pith_number":"pith:KDQFKFGL","canonical_record":{"source":{"id":"2010.00654","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-01T19:28:28Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"a17698690db2dc4d93eb67b3a328b44a8720a084c8018700642517c4692b42bd","abstract_canon_sha256":"d2197a98e36d64c6ef28cdd8cc96abfe373b18653c47188c7435c7aa0a6a2fd0"},"schema_version":"1.0"},"canonical_sha256":"50e05514cb4afa041b07e6fba10f6d68a2b575047bdb98041053440b7c8e59a0","source":{"kind":"arxiv","id":"2010.00654","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.00654","created_at":"2026-07-05T03:29:14Z"},{"alias_kind":"arxiv_version","alias_value":"2010.00654v3","created_at":"2026-07-05T03:29:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.00654","created_at":"2026-07-05T03:29:14Z"},{"alias_kind":"pith_short_12","alias_value":"KDQFKFGLJL5A","created_at":"2026-07-05T03:29:14Z"},{"alias_kind":"pith_short_16","alias_value":"KDQFKFGLJL5AIGYH","created_at":"2026-07-05T03:29:14Z"},{"alias_kind":"pith_short_8","alias_value":"KDQFKFGL","created_at":"2026-07-05T03:29:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:KDQFKFGLJL5AIGYH4352CD3NNC","target":"record","payload":{"canonical_record":{"source":{"id":"2010.00654","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-01T19:28:28Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"a17698690db2dc4d93eb67b3a328b44a8720a084c8018700642517c4692b42bd","abstract_canon_sha256":"d2197a98e36d64c6ef28cdd8cc96abfe373b18653c47188c7435c7aa0a6a2fd0"},"schema_version":"1.0"},"canonical_sha256":"50e05514cb4afa041b07e6fba10f6d68a2b575047bdb98041053440b7c8e59a0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:29:14.217064Z","signature_b64":"Sjp2DxGGvYWqn+qoWDwk3ERp8ZUZcBTB8x2F82H2x2SfwFBWqMDOu+4yKB/g1vbzYuto91mMyy3DOF6YMuNZBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"50e05514cb4afa041b07e6fba10f6d68a2b575047bdb98041053440b7c8e59a0","last_reissued_at":"2026-07-05T03:29:14.216620Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:29:14.216620Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2010.00654","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:29:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uOyKyflXSeLAYf7fSj1GqtsZEnPq5OyzOorRoP1Ri+OsdPFCKVzoGYbNcX53bDyWldOC7ejSgzgUTCaxmpPDDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T15:01:56.195718Z"},"content_sha256":"61d8c7e21c34d33f6d6f151330e266c75a923d49425bcaed09ed35d07cca4479","schema_version":"1.0","event_id":"sha256:61d8c7e21c34d33f6d6f151330e266c75a923d49425bcaed09ed35d07cca4479"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:KDQFKFGLJL5AIGYH4352CD3NNC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Arash Vahdat, Jan Kautz, Karsten Kreis, Zhisheng Xiao","submitted_at":"2020-10-01T19:28:28Z","abstract_excerpt":"Energy-based models (EBMs) have recently been successful in representing complex distributions of small images. However, sampling from them requires expensive Markov chain Monte Carlo (MCMC) iterations that mix slowly in high dimensional pixel space. Unlike EBMs, variational autoencoders (VAEs) generate samples quickly and are equipped with a latent space that enables fast traversal of the data manifold. However, VAEs tend to assign high probability density to regions in data space outside the actual data distribution and often fail at generating sharp images. In this paper, we propose VAEBM, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.00654","kind":"arxiv","version":3},"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/2010.00654/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":"2026-07-05T03:29:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rjMj+S0p5iCR0Evybkes3BLA2oaMt2i7lD3kIWoFDoY1SjAZcJbsJAoOxCk53/5rcTzkliekupnETkMGVimBBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T15:01:56.196106Z"},"content_sha256":"cc98cd01c593ea094a4c2c618d81ebc5f52cf6505e678d4159d70039c340b14f","schema_version":"1.0","event_id":"sha256:cc98cd01c593ea094a4c2c618d81ebc5f52cf6505e678d4159d70039c340b14f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KDQFKFGLJL5AIGYH4352CD3NNC/bundle.json","state_url":"https://pith.science/pith/KDQFKFGLJL5AIGYH4352CD3NNC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KDQFKFGLJL5AIGYH4352CD3NNC/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":"2026-07-06T15:01:56Z","links":{"resolver":"https://pith.science/pith/KDQFKFGLJL5AIGYH4352CD3NNC","bundle":"https://pith.science/pith/KDQFKFGLJL5AIGYH4352CD3NNC/bundle.json","state":"https://pith.science/pith/KDQFKFGLJL5AIGYH4352CD3NNC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KDQFKFGLJL5AIGYH4352CD3NNC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:KDQFKFGLJL5AIGYH4352CD3NNC","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":"d2197a98e36d64c6ef28cdd8cc96abfe373b18653c47188c7435c7aa0a6a2fd0","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-01T19:28:28Z","title_canon_sha256":"a17698690db2dc4d93eb67b3a328b44a8720a084c8018700642517c4692b42bd"},"schema_version":"1.0","source":{"id":"2010.00654","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.00654","created_at":"2026-07-05T03:29:14Z"},{"alias_kind":"arxiv_version","alias_value":"2010.00654v3","created_at":"2026-07-05T03:29:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.00654","created_at":"2026-07-05T03:29:14Z"},{"alias_kind":"pith_short_12","alias_value":"KDQFKFGLJL5A","created_at":"2026-07-05T03:29:14Z"},{"alias_kind":"pith_short_16","alias_value":"KDQFKFGLJL5AIGYH","created_at":"2026-07-05T03:29:14Z"},{"alias_kind":"pith_short_8","alias_value":"KDQFKFGL","created_at":"2026-07-05T03:29:14Z"}],"graph_snapshots":[{"event_id":"sha256:cc98cd01c593ea094a4c2c618d81ebc5f52cf6505e678d4159d70039c340b14f","target":"graph","created_at":"2026-07-05T03:29:14Z","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/2010.00654/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Energy-based models (EBMs) have recently been successful in representing complex distributions of small images. However, sampling from them requires expensive Markov chain Monte Carlo (MCMC) iterations that mix slowly in high dimensional pixel space. Unlike EBMs, variational autoencoders (VAEs) generate samples quickly and are equipped with a latent space that enables fast traversal of the data manifold. However, VAEs tend to assign high probability density to regions in data space outside the actual data distribution and often fail at generating sharp images. In this paper, we propose VAEBM, ","authors_text":"Arash Vahdat, Jan Kautz, Karsten Kreis, Zhisheng Xiao","cross_cats":["cs.CV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-01T19:28:28Z","title":"VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.00654","kind":"arxiv","version":3},"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":"sha256:61d8c7e21c34d33f6d6f151330e266c75a923d49425bcaed09ed35d07cca4479","target":"record","created_at":"2026-07-05T03:29:14Z","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":"d2197a98e36d64c6ef28cdd8cc96abfe373b18653c47188c7435c7aa0a6a2fd0","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-01T19:28:28Z","title_canon_sha256":"a17698690db2dc4d93eb67b3a328b44a8720a084c8018700642517c4692b42bd"},"schema_version":"1.0","source":{"id":"2010.00654","kind":"arxiv","version":3}},"canonical_sha256":"50e05514cb4afa041b07e6fba10f6d68a2b575047bdb98041053440b7c8e59a0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"50e05514cb4afa041b07e6fba10f6d68a2b575047bdb98041053440b7c8e59a0","first_computed_at":"2026-07-05T03:29:14.216620Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:29:14.216620Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Sjp2DxGGvYWqn+qoWDwk3ERp8ZUZcBTB8x2F82H2x2SfwFBWqMDOu+4yKB/g1vbzYuto91mMyy3DOF6YMuNZBA==","signature_status":"signed_v1","signed_at":"2026-07-05T03:29:14.217064Z","signed_message":"canonical_sha256_bytes"},"source_id":"2010.00654","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:61d8c7e21c34d33f6d6f151330e266c75a923d49425bcaed09ed35d07cca4479","sha256:cc98cd01c593ea094a4c2c618d81ebc5f52cf6505e678d4159d70039c340b14f"],"state_sha256":"7d2c2ea41fe4c351e9cfd53604af85442b3f397b36e21382c429aa4f074c157a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2YcdaQJAgpZehBJw+jaTv9N3Rln80nizY7Rq7I3WxH9QMni8Y0r9nJnqKQOjSwz26BIArUVreKJoUFP++XYQCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T15:01:56.198092Z","bundle_sha256":"669a3e23199eed0c18924f25e563a125136a0976e5d31083207ab154ab08ee2e"}}