{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:UU2NEEQ5ZWQ3BRVRTN7CNTXQQB","short_pith_number":"pith:UU2NEEQ5","canonical_record":{"source":{"id":"1903.08689","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-20T18:34:29Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"bdf852576fc141254d520867ffef804beed4301a073d49c0665e76ba6b3cc1bb","abstract_canon_sha256":"995ad292d9f2f52faa535348a327c713618f4d39055adabd82103bd615210408"},"schema_version":"1.0"},"canonical_sha256":"a534d2121dcda1b0c6b19b7e26cef08063044aafd4f8a1631fa03d0809fce363","source":{"kind":"arxiv","id":"1903.08689","version":6},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.08689","created_at":"2026-07-05T01:14:23Z"},{"alias_kind":"arxiv_version","alias_value":"1903.08689v6","created_at":"2026-07-05T01:14:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.08689","created_at":"2026-07-05T01:14:23Z"},{"alias_kind":"pith_short_12","alias_value":"UU2NEEQ5ZWQ3","created_at":"2026-07-05T01:14:23Z"},{"alias_kind":"pith_short_16","alias_value":"UU2NEEQ5ZWQ3BRVR","created_at":"2026-07-05T01:14:23Z"},{"alias_kind":"pith_short_8","alias_value":"UU2NEEQ5","created_at":"2026-07-05T01:14:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:UU2NEEQ5ZWQ3BRVRTN7CNTXQQB","target":"record","payload":{"canonical_record":{"source":{"id":"1903.08689","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-20T18:34:29Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"bdf852576fc141254d520867ffef804beed4301a073d49c0665e76ba6b3cc1bb","abstract_canon_sha256":"995ad292d9f2f52faa535348a327c713618f4d39055adabd82103bd615210408"},"schema_version":"1.0"},"canonical_sha256":"a534d2121dcda1b0c6b19b7e26cef08063044aafd4f8a1631fa03d0809fce363","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:14:23.890910Z","signature_b64":"UPN++PSX1/fgZBHTctBD+scdobuNGBicip672mariPhJU1KYPrTYMriyU1dVFGYG9ui+zepqaGu5As7wf5x7Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a534d2121dcda1b0c6b19b7e26cef08063044aafd4f8a1631fa03d0809fce363","last_reissued_at":"2026-07-05T01:14:23.890472Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:14:23.890472Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1903.08689","source_version":6,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:14:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MMqSgADFajM793hk8r655TN8m5jErxmVIAXUf8DtMYJILcKZmD+lnAbiyHvbCsHbJnv+ct6ZhbN0F1awgkKWDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T04:32:11.872932Z"},"content_sha256":"dc9afcac9712233bd2314c221e4fd5a12f6273c25fa9f5b2c965d87cc7c57555","schema_version":"1.0","event_id":"sha256:dc9afcac9712233bd2314c221e4fd5a12f6273c25fa9f5b2c965d87cc7c57555"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:UU2NEEQ5ZWQ3BRVRTN7CNTXQQB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Implicit Generation and Generalization in 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":"Igor Mordatch, Yilun Du","submitted_at":"2019-03-20T18:34:29Z","abstract_excerpt":"Energy based models (EBMs) are appealing due to their generality and simplicity in likelihood modeling, but have been traditionally difficult to train. We present techniques to scale MCMC based EBM training on continuous neural networks, and we show its success on the high-dimensional data domains of ImageNet32x32, ImageNet128x128, CIFAR-10, and robotic hand trajectories, achieving better samples than other likelihood models and nearing the performance of contemporary GAN approaches, while covering all modes of the data. We highlight some unique capabilities of implicit generation such as comp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.08689","kind":"arxiv","version":6},"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/1903.08689/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":"2026-07-05T01:14:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7134wEFZhbN3ssBdvB1nyOP0+SsGjeThr54zQjkJj1S2OEqyETk+jUjySCyMwB1ST+7ujTQgiurxsztJHXcZDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T04:32:11.873555Z"},"content_sha256":"3237b5273991f0e02e68f53fb74d0889577fdfac3092922295730ec657084825","schema_version":"1.0","event_id":"sha256:3237b5273991f0e02e68f53fb74d0889577fdfac3092922295730ec657084825"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UU2NEEQ5ZWQ3BRVRTN7CNTXQQB/bundle.json","state_url":"https://pith.science/pith/UU2NEEQ5ZWQ3BRVRTN7CNTXQQB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UU2NEEQ5ZWQ3BRVRTN7CNTXQQB/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":"2026-08-06T04:32:11Z","links":{"resolver":"https://pith.science/pith/UU2NEEQ5ZWQ3BRVRTN7CNTXQQB","bundle":"https://pith.science/pith/UU2NEEQ5ZWQ3BRVRTN7CNTXQQB/bundle.json","state":"https://pith.science/pith/UU2NEEQ5ZWQ3BRVRTN7CNTXQQB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UU2NEEQ5ZWQ3BRVRTN7CNTXQQB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:UU2NEEQ5ZWQ3BRVRTN7CNTXQQB","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":"995ad292d9f2f52faa535348a327c713618f4d39055adabd82103bd615210408","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-20T18:34:29Z","title_canon_sha256":"bdf852576fc141254d520867ffef804beed4301a073d49c0665e76ba6b3cc1bb"},"schema_version":"1.0","source":{"id":"1903.08689","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.08689","created_at":"2026-07-05T01:14:23Z"},{"alias_kind":"arxiv_version","alias_value":"1903.08689v6","created_at":"2026-07-05T01:14:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.08689","created_at":"2026-07-05T01:14:23Z"},{"alias_kind":"pith_short_12","alias_value":"UU2NEEQ5ZWQ3","created_at":"2026-07-05T01:14:23Z"},{"alias_kind":"pith_short_16","alias_value":"UU2NEEQ5ZWQ3BRVR","created_at":"2026-07-05T01:14:23Z"},{"alias_kind":"pith_short_8","alias_value":"UU2NEEQ5","created_at":"2026-07-05T01:14:23Z"}],"graph_snapshots":[{"event_id":"sha256:3237b5273991f0e02e68f53fb74d0889577fdfac3092922295730ec657084825","target":"graph","created_at":"2026-07-05T01:14:23Z","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/1903.08689/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Energy based models (EBMs) are appealing due to their generality and simplicity in likelihood modeling, but have been traditionally difficult to train. We present techniques to scale MCMC based EBM training on continuous neural networks, and we show its success on the high-dimensional data domains of ImageNet32x32, ImageNet128x128, CIFAR-10, and robotic hand trajectories, achieving better samples than other likelihood models and nearing the performance of contemporary GAN approaches, while covering all modes of the data. We highlight some unique capabilities of implicit generation such as comp","authors_text":"Igor Mordatch, Yilun Du","cross_cats":["cs.CV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-20T18:34:29Z","title":"Implicit Generation and Generalization in Energy-Based Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.08689","kind":"arxiv","version":6},"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:dc9afcac9712233bd2314c221e4fd5a12f6273c25fa9f5b2c965d87cc7c57555","target":"record","created_at":"2026-07-05T01:14:23Z","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":"995ad292d9f2f52faa535348a327c713618f4d39055adabd82103bd615210408","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-20T18:34:29Z","title_canon_sha256":"bdf852576fc141254d520867ffef804beed4301a073d49c0665e76ba6b3cc1bb"},"schema_version":"1.0","source":{"id":"1903.08689","kind":"arxiv","version":6}},"canonical_sha256":"a534d2121dcda1b0c6b19b7e26cef08063044aafd4f8a1631fa03d0809fce363","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a534d2121dcda1b0c6b19b7e26cef08063044aafd4f8a1631fa03d0809fce363","first_computed_at":"2026-07-05T01:14:23.890472Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:14:23.890472Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UPN++PSX1/fgZBHTctBD+scdobuNGBicip672mariPhJU1KYPrTYMriyU1dVFGYG9ui+zepqaGu5As7wf5x7Aw==","signature_status":"signed_v1","signed_at":"2026-07-05T01:14:23.890910Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.08689","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dc9afcac9712233bd2314c221e4fd5a12f6273c25fa9f5b2c965d87cc7c57555","sha256:3237b5273991f0e02e68f53fb74d0889577fdfac3092922295730ec657084825"],"state_sha256":"4ba6e517466ea7b03bf8784fd3cf182ffa8ae86811a09c49d9bc955df783122b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YNS0oeBezkEQUSyalZuBtFV0geneHlUzXn4VnBZ3mPVxQM2yZiK36UfHLCl45gkBKLJPLK7uoQtZa2+vq0kUAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T04:32:11.877619Z","bundle_sha256":"aea006dbcd8c583e29f2da34af5090afa0f76bae070fc772286516cb45015ae6"}}