{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:AVT3JOND5SLQ4UINRM2FCWFR6L","short_pith_number":"pith:AVT3JOND","canonical_record":{"source":{"id":"1904.05626","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-04-11T11:11:01Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"208c3a2f2782c9ea694d98a59e51c32a586c03ae2de9790f238dd4ff405ae569","abstract_canon_sha256":"862324496b6382d3062110f663bc90ab7c59f78c7b1f28428b20dfcc97513ed5"},"schema_version":"1.0"},"canonical_sha256":"0567b4b9a3ec970e510d8b345158b1f2d93b68b9ca5327778820109e9dff071e","source":{"kind":"arxiv","id":"1904.05626","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.05626","created_at":"2026-05-17T23:48:48Z"},{"alias_kind":"arxiv_version","alias_value":"1904.05626v1","created_at":"2026-05-17T23:48:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.05626","created_at":"2026-05-17T23:48:48Z"},{"alias_kind":"pith_short_12","alias_value":"AVT3JOND5SLQ","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"AVT3JOND5SLQ4UIN","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"AVT3JOND","created_at":"2026-05-18T12:33:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:AVT3JOND5SLQ4UINRM2FCWFR6L","target":"record","payload":{"canonical_record":{"source":{"id":"1904.05626","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-04-11T11:11:01Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"208c3a2f2782c9ea694d98a59e51c32a586c03ae2de9790f238dd4ff405ae569","abstract_canon_sha256":"862324496b6382d3062110f663bc90ab7c59f78c7b1f28428b20dfcc97513ed5"},"schema_version":"1.0"},"canonical_sha256":"0567b4b9a3ec970e510d8b345158b1f2d93b68b9ca5327778820109e9dff071e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:48.906551Z","signature_b64":"8ohUBEFmViGBuL3u/Cnrbhalac9CYg6ebIbtpdw2rKH7HuXJbQWimkJ/mE2w6yhilePu145K+b0SCsohuOVrDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0567b4b9a3ec970e510d8b345158b1f2d93b68b9ca5327778820109e9dff071e","last_reissued_at":"2026-05-17T23:48:48.906095Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:48.906095Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1904.05626","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":"2026-05-17T23:48:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aZLR9t3ZNeDpxX6CndpakujKMa/0uQlp/FpGY9DZmt3bL2jP9upXoLYh1ahLOiKTktZ0IY0wMH7ST3uzOQEtBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T05:36:56.671496Z"},"content_sha256":"040dccf55b184fd64e7bf41e0203d1a10ba5ff106767115bb164a6b0f230afcd","schema_version":"1.0","event_id":"sha256:040dccf55b184fd64e7bf41e0203d1a10ba5ff106767115bb164a6b0f230afcd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:AVT3JOND5SLQ4UINRM2FCWFR6L","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Autoregressive Energy Machines","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Charlie Nash, Conor Durkan","submitted_at":"2019-04-11T11:11:01Z","abstract_excerpt":"Neural density estimators are flexible families of parametric models which have seen widespread use in unsupervised machine learning in recent years. Maximum-likelihood training typically dictates that these models be constrained to specify an explicit density. However, this limitation can be overcome by instead using a neural network to specify an energy function, or unnormalized density, which can subsequently be normalized to obtain a valid distribution. The challenge with this approach lies in accurately estimating the normalizing constant of the high-dimensional energy function. We propos"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.05626","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":"2026-05-17T23:48:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xsg4L4Y+Nt0Ei0t4jxFRQvJIXgaBp6abjGUwKP3BitEuu0FuweIn14sj0cfHmzu5qUl643nrNZEOyeBP8JQICA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T05:36:56.672226Z"},"content_sha256":"9ad53cfc2eade18869494f592e4d77a646c47dcf950e52bc37b74ac718235699","schema_version":"1.0","event_id":"sha256:9ad53cfc2eade18869494f592e4d77a646c47dcf950e52bc37b74ac718235699"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AVT3JOND5SLQ4UINRM2FCWFR6L/bundle.json","state_url":"https://pith.science/pith/AVT3JOND5SLQ4UINRM2FCWFR6L/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AVT3JOND5SLQ4UINRM2FCWFR6L/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":"2026-06-09T05:36:56Z","links":{"resolver":"https://pith.science/pith/AVT3JOND5SLQ4UINRM2FCWFR6L","bundle":"https://pith.science/pith/AVT3JOND5SLQ4UINRM2FCWFR6L/bundle.json","state":"https://pith.science/pith/AVT3JOND5SLQ4UINRM2FCWFR6L/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AVT3JOND5SLQ4UINRM2FCWFR6L/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:AVT3JOND5SLQ4UINRM2FCWFR6L","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":"862324496b6382d3062110f663bc90ab7c59f78c7b1f28428b20dfcc97513ed5","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-04-11T11:11:01Z","title_canon_sha256":"208c3a2f2782c9ea694d98a59e51c32a586c03ae2de9790f238dd4ff405ae569"},"schema_version":"1.0","source":{"id":"1904.05626","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.05626","created_at":"2026-05-17T23:48:48Z"},{"alias_kind":"arxiv_version","alias_value":"1904.05626v1","created_at":"2026-05-17T23:48:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.05626","created_at":"2026-05-17T23:48:48Z"},{"alias_kind":"pith_short_12","alias_value":"AVT3JOND5SLQ","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"AVT3JOND5SLQ4UIN","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"AVT3JOND","created_at":"2026-05-18T12:33:12Z"}],"graph_snapshots":[{"event_id":"sha256:9ad53cfc2eade18869494f592e4d77a646c47dcf950e52bc37b74ac718235699","target":"graph","created_at":"2026-05-17T23:48:48Z","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":"Neural density estimators are flexible families of parametric models which have seen widespread use in unsupervised machine learning in recent years. Maximum-likelihood training typically dictates that these models be constrained to specify an explicit density. However, this limitation can be overcome by instead using a neural network to specify an energy function, or unnormalized density, which can subsequently be normalized to obtain a valid distribution. The challenge with this approach lies in accurately estimating the normalizing constant of the high-dimensional energy function. We propos","authors_text":"Charlie Nash, Conor Durkan","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-04-11T11:11:01Z","title":"Autoregressive Energy Machines"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.05626","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":"sha256:040dccf55b184fd64e7bf41e0203d1a10ba5ff106767115bb164a6b0f230afcd","target":"record","created_at":"2026-05-17T23:48:48Z","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":"862324496b6382d3062110f663bc90ab7c59f78c7b1f28428b20dfcc97513ed5","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-04-11T11:11:01Z","title_canon_sha256":"208c3a2f2782c9ea694d98a59e51c32a586c03ae2de9790f238dd4ff405ae569"},"schema_version":"1.0","source":{"id":"1904.05626","kind":"arxiv","version":1}},"canonical_sha256":"0567b4b9a3ec970e510d8b345158b1f2d93b68b9ca5327778820109e9dff071e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0567b4b9a3ec970e510d8b345158b1f2d93b68b9ca5327778820109e9dff071e","first_computed_at":"2026-05-17T23:48:48.906095Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:48:48.906095Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8ohUBEFmViGBuL3u/Cnrbhalac9CYg6ebIbtpdw2rKH7HuXJbQWimkJ/mE2w6yhilePu145K+b0SCsohuOVrDA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:48:48.906551Z","signed_message":"canonical_sha256_bytes"},"source_id":"1904.05626","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:040dccf55b184fd64e7bf41e0203d1a10ba5ff106767115bb164a6b0f230afcd","sha256:9ad53cfc2eade18869494f592e4d77a646c47dcf950e52bc37b74ac718235699"],"state_sha256":"dcbebc03937fbbc3b6149e9ab2cb7930bf6a26e040b3ade61f18bda3a9791467"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7mxZv1BgNmLfqoOyPDYulbSIsondEya6U6ai4ZxeyKeQJUi7uMLZjVsUrhdQazcPp7rKDAwfms7wuxDdnBDbBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-09T05:36:56.676359Z","bundle_sha256":"682341851e88d73c759c9256121c2065b82ec6ded3f0ac3ba01642da00f6a56e"}}