{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:IPLAE4Y2CWZWEOSKDPJRVTVDVR","short_pith_number":"pith:IPLAE4Y2","canonical_record":{"source":{"id":"1904.09770","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-04-22T08:48:52Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ac5ebbebd20462fd40271197f1b9a861c8f42642c9ee3a3c70edf07430ee2273","abstract_canon_sha256":"0f3f504910baed1aae09386ae9ff6b0b4fb10ec8ef7dc714a44e84399477c8dc"},"schema_version":"1.0"},"canonical_sha256":"43d602731a15b3623a4a1bd31acea3ac7cb70262e6c3a35c2ca320f3390f59cb","source":{"kind":"arxiv","id":"1904.09770","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.09770","created_at":"2026-07-05T00:21:56Z"},{"alias_kind":"arxiv_version","alias_value":"1904.09770v4","created_at":"2026-07-05T00:21:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.09770","created_at":"2026-07-05T00:21:56Z"},{"alias_kind":"pith_short_12","alias_value":"IPLAE4Y2CWZW","created_at":"2026-07-05T00:21:56Z"},{"alias_kind":"pith_short_16","alias_value":"IPLAE4Y2CWZWEOSK","created_at":"2026-07-05T00:21:56Z"},{"alias_kind":"pith_short_8","alias_value":"IPLAE4Y2","created_at":"2026-07-05T00:21:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:IPLAE4Y2CWZWEOSKDPJRVTVDVR","target":"record","payload":{"canonical_record":{"source":{"id":"1904.09770","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-04-22T08:48:52Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ac5ebbebd20462fd40271197f1b9a861c8f42642c9ee3a3c70edf07430ee2273","abstract_canon_sha256":"0f3f504910baed1aae09386ae9ff6b0b4fb10ec8ef7dc714a44e84399477c8dc"},"schema_version":"1.0"},"canonical_sha256":"43d602731a15b3623a4a1bd31acea3ac7cb70262e6c3a35c2ca320f3390f59cb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:21:56.438279Z","signature_b64":"DadTD+OpXfSoEL9wva/18/ZsWgLGPn7S60QJY4DORnPxH11PAPfl6VToYw2Smbnb8yzxdrhk6RIcNk0tFA0lCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"43d602731a15b3623a4a1bd31acea3ac7cb70262e6c3a35c2ca320f3390f59cb","last_reissued_at":"2026-07-05T00:21:56.437804Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:21:56.437804Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1904.09770","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":"2026-07-05T00:21:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7d9HjmqjbIJaesB9Aujhtvdn07eskAFjeXpGBwf3aXmX+aJ/OrjOHOnW4AmRKD/9PjHdmgq2TnYAWy2An4SZCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T21:51:21.576732Z"},"content_sha256":"af1674bdc9da768554f47a0f4a6e1561330cb58617903468a123767669b6f16e","schema_version":"1.0","event_id":"sha256:af1674bdc9da768554f47a0f4a6e1561330cb58617903468a123767669b6f16e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:IPLAE4Y2CWZWEOSKDPJRVTVDVR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Erik Nijkamp, Mitch Hill, Song-Chun Zhu, Ying Nian Wu","submitted_at":"2019-04-22T08:48:52Z","abstract_excerpt":"This paper studies a curious phenomenon in learning energy-based model (EBM) using MCMC. In each learning iteration, we generate synthesized examples by running a non-convergent, non-mixing, and non-persistent short-run MCMC toward the current model, always starting from the same initial distribution such as uniform noise distribution, and always running a fixed number of MCMC steps. After generating synthesized examples, we then update the model parameters according to the maximum likelihood learning gradient, as if the synthesized examples are fair samples from the current model. We treat th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.09770","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/1904.09770/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":"2026-07-05T00:21:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kAbPsumqnYLQPlj8qHe4lztCOUz2x8ZkVKhQtB6R12X/bR60DywKl097qZg1PWQOSx6IHIdfZVZczja1nU5vCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T21:51:21.577668Z"},"content_sha256":"754c4906bd8d50761aed3d44165a354e4708c7fcfa17ef40b9881c2eee0f71aa","schema_version":"1.0","event_id":"sha256:754c4906bd8d50761aed3d44165a354e4708c7fcfa17ef40b9881c2eee0f71aa"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IPLAE4Y2CWZWEOSKDPJRVTVDVR/bundle.json","state_url":"https://pith.science/pith/IPLAE4Y2CWZWEOSKDPJRVTVDVR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IPLAE4Y2CWZWEOSKDPJRVTVDVR/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":"2026-08-14T21:51:21Z","links":{"resolver":"https://pith.science/pith/IPLAE4Y2CWZWEOSKDPJRVTVDVR","bundle":"https://pith.science/pith/IPLAE4Y2CWZWEOSKDPJRVTVDVR/bundle.json","state":"https://pith.science/pith/IPLAE4Y2CWZWEOSKDPJRVTVDVR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IPLAE4Y2CWZWEOSKDPJRVTVDVR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:IPLAE4Y2CWZWEOSKDPJRVTVDVR","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":"0f3f504910baed1aae09386ae9ff6b0b4fb10ec8ef7dc714a44e84399477c8dc","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-04-22T08:48:52Z","title_canon_sha256":"ac5ebbebd20462fd40271197f1b9a861c8f42642c9ee3a3c70edf07430ee2273"},"schema_version":"1.0","source":{"id":"1904.09770","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.09770","created_at":"2026-07-05T00:21:56Z"},{"alias_kind":"arxiv_version","alias_value":"1904.09770v4","created_at":"2026-07-05T00:21:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.09770","created_at":"2026-07-05T00:21:56Z"},{"alias_kind":"pith_short_12","alias_value":"IPLAE4Y2CWZW","created_at":"2026-07-05T00:21:56Z"},{"alias_kind":"pith_short_16","alias_value":"IPLAE4Y2CWZWEOSK","created_at":"2026-07-05T00:21:56Z"},{"alias_kind":"pith_short_8","alias_value":"IPLAE4Y2","created_at":"2026-07-05T00:21:56Z"}],"graph_snapshots":[{"event_id":"sha256:754c4906bd8d50761aed3d44165a354e4708c7fcfa17ef40b9881c2eee0f71aa","target":"graph","created_at":"2026-07-05T00:21:56Z","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/1904.09770/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper studies a curious phenomenon in learning energy-based model (EBM) using MCMC. In each learning iteration, we generate synthesized examples by running a non-convergent, non-mixing, and non-persistent short-run MCMC toward the current model, always starting from the same initial distribution such as uniform noise distribution, and always running a fixed number of MCMC steps. After generating synthesized examples, we then update the model parameters according to the maximum likelihood learning gradient, as if the synthesized examples are fair samples from the current model. We treat th","authors_text":"Erik Nijkamp, Mitch Hill, Song-Chun Zhu, Ying Nian Wu","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-04-22T08:48:52Z","title":"Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.09770","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:af1674bdc9da768554f47a0f4a6e1561330cb58617903468a123767669b6f16e","target":"record","created_at":"2026-07-05T00:21:56Z","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":"0f3f504910baed1aae09386ae9ff6b0b4fb10ec8ef7dc714a44e84399477c8dc","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-04-22T08:48:52Z","title_canon_sha256":"ac5ebbebd20462fd40271197f1b9a861c8f42642c9ee3a3c70edf07430ee2273"},"schema_version":"1.0","source":{"id":"1904.09770","kind":"arxiv","version":4}},"canonical_sha256":"43d602731a15b3623a4a1bd31acea3ac7cb70262e6c3a35c2ca320f3390f59cb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"43d602731a15b3623a4a1bd31acea3ac7cb70262e6c3a35c2ca320f3390f59cb","first_computed_at":"2026-07-05T00:21:56.437804Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:21:56.437804Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DadTD+OpXfSoEL9wva/18/ZsWgLGPn7S60QJY4DORnPxH11PAPfl6VToYw2Smbnb8yzxdrhk6RIcNk0tFA0lCg==","signature_status":"signed_v1","signed_at":"2026-07-05T00:21:56.438279Z","signed_message":"canonical_sha256_bytes"},"source_id":"1904.09770","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:af1674bdc9da768554f47a0f4a6e1561330cb58617903468a123767669b6f16e","sha256:754c4906bd8d50761aed3d44165a354e4708c7fcfa17ef40b9881c2eee0f71aa"],"state_sha256":"1106729c077b1a2412aa1e528309d2232ab7bcbfe0df51d8b9e27ae17057a167"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QwzZkKErgHyT1MXahPTV2OfSwPjmL9+16t01yk9cQggq4xwfNCgjN4jG8CVq9LEP09jOXKbfnaluxJEOliALAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T21:51:21.588967Z","bundle_sha256":"ce9db0eb1c1944b532b708bcf64cf77a9b1e91371386a24cfb7864fd6d22ba30"}}