{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:QXYOR7RYE4MMP66YQVBNPBXRP6","short_pith_number":"pith:QXYOR7RY","canonical_record":{"source":{"id":"1703.03664","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-10T12:58:23Z","cross_cats_sorted":["cs.NE"],"title_canon_sha256":"8583877b164b0bcb16ec090ac89e02fe1839e3fe62a45c57865c33637d19259f","abstract_canon_sha256":"4bbcfb57b66fa9eee9c0f7e0aeddebd43d1afad72b64333dabc500b6a791f8fd"},"schema_version":"1.0"},"canonical_sha256":"85f0e8fe382718c7fbd88542d786f17f8cdf819486584f62eced73a8ab3b9a85","source":{"kind":"arxiv","id":"1703.03664","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.03664","created_at":"2026-05-18T00:48:58Z"},{"alias_kind":"arxiv_version","alias_value":"1703.03664v1","created_at":"2026-05-18T00:48:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.03664","created_at":"2026-05-18T00:48:58Z"},{"alias_kind":"pith_short_12","alias_value":"QXYOR7RYE4MM","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"QXYOR7RYE4MMP66Y","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"QXYOR7RY","created_at":"2026-05-18T12:31:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:QXYOR7RYE4MMP66YQVBNPBXRP6","target":"record","payload":{"canonical_record":{"source":{"id":"1703.03664","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-10T12:58:23Z","cross_cats_sorted":["cs.NE"],"title_canon_sha256":"8583877b164b0bcb16ec090ac89e02fe1839e3fe62a45c57865c33637d19259f","abstract_canon_sha256":"4bbcfb57b66fa9eee9c0f7e0aeddebd43d1afad72b64333dabc500b6a791f8fd"},"schema_version":"1.0"},"canonical_sha256":"85f0e8fe382718c7fbd88542d786f17f8cdf819486584f62eced73a8ab3b9a85","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:48:58.399964Z","signature_b64":"W9g8rP6c2zXftwRTaSwjhj7wsJpM6nNRo4aoxCDmW3tCIr+aEKa1ga4hLnjhSQf+vyWOqdL1JZ0kK6P7Y1TJCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"85f0e8fe382718c7fbd88542d786f17f8cdf819486584f62eced73a8ab3b9a85","last_reissued_at":"2026-05-18T00:48:58.399296Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:48:58.399296Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1703.03664","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":"2026-05-18T00:48:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"E1y77LDhh/waH9ttk5i9XbrudnyqiTBjijy97xRFJ8sOo9cKKhh2qAhDd+C8j4V/l9YavgDuxakOqgSbuqVmCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-18T20:08:34.475094Z"},"content_sha256":"5fca81860a4eccc130dfb8ce87021b7daba3b4063dd22f8f1642bb63d2e1c460","schema_version":"1.0","event_id":"sha256:5fca81860a4eccc130dfb8ce87021b7daba3b4063dd22f8f1642bb63d2e1c460"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:QXYOR7RYE4MMP66YQVBNPBXRP6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Parallel Multiscale Autoregressive Density Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.CV","authors_text":"A\\\"aron van den Oord, Dan Belov, Nal Kalchbrenner, Nando de Freitas, Scott Reed, Sergio G\\'omez Colmenarejo, Ziyu Wang","submitted_at":"2017-03-10T12:58:23Z","abstract_excerpt":"PixelCNN achieves state-of-the-art results in density estimation for natural images. Although training is fast, inference is costly, requiring one network evaluation per pixel; O(N) for N pixels. This can be sped up by caching activations, but still involves generating each pixel sequentially. In this work, we propose a parallelized PixelCNN that allows more efficient inference by modeling certain pixel groups as conditionally independent. Our new PixelCNN model achieves competitive density estimation and orders of magnitude speedup - O(log N) sampling instead of O(N) - enabling the practical "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.03664","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":"2026-05-18T00:48:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"beqhFXyelHqkiOK04O/HokDsA242up5bITNPyDJhy5PGjSHfIfr8EzVocy6SM0IqF7Zq/ITHEcNQKR45zHqNDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-18T20:08:34.475788Z"},"content_sha256":"9503d68e27bee06e3123bcfe44c218b532afccbaff841236dfa1f81a8054e556","schema_version":"1.0","event_id":"sha256:9503d68e27bee06e3123bcfe44c218b532afccbaff841236dfa1f81a8054e556"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QXYOR7RYE4MMP66YQVBNPBXRP6/bundle.json","state_url":"https://pith.science/pith/QXYOR7RYE4MMP66YQVBNPBXRP6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QXYOR7RYE4MMP66YQVBNPBXRP6/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":"2026-05-18T20:08:34Z","links":{"resolver":"https://pith.science/pith/QXYOR7RYE4MMP66YQVBNPBXRP6","bundle":"https://pith.science/pith/QXYOR7RYE4MMP66YQVBNPBXRP6/bundle.json","state":"https://pith.science/pith/QXYOR7RYE4MMP66YQVBNPBXRP6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QXYOR7RYE4MMP66YQVBNPBXRP6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:QXYOR7RYE4MMP66YQVBNPBXRP6","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":"4bbcfb57b66fa9eee9c0f7e0aeddebd43d1afad72b64333dabc500b6a791f8fd","cross_cats_sorted":["cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-10T12:58:23Z","title_canon_sha256":"8583877b164b0bcb16ec090ac89e02fe1839e3fe62a45c57865c33637d19259f"},"schema_version":"1.0","source":{"id":"1703.03664","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.03664","created_at":"2026-05-18T00:48:58Z"},{"alias_kind":"arxiv_version","alias_value":"1703.03664v1","created_at":"2026-05-18T00:48:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.03664","created_at":"2026-05-18T00:48:58Z"},{"alias_kind":"pith_short_12","alias_value":"QXYOR7RYE4MM","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"QXYOR7RYE4MMP66Y","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"QXYOR7RY","created_at":"2026-05-18T12:31:39Z"}],"graph_snapshots":[{"event_id":"sha256:9503d68e27bee06e3123bcfe44c218b532afccbaff841236dfa1f81a8054e556","target":"graph","created_at":"2026-05-18T00:48:58Z","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":"PixelCNN achieves state-of-the-art results in density estimation for natural images. Although training is fast, inference is costly, requiring one network evaluation per pixel; O(N) for N pixels. This can be sped up by caching activations, but still involves generating each pixel sequentially. In this work, we propose a parallelized PixelCNN that allows more efficient inference by modeling certain pixel groups as conditionally independent. Our new PixelCNN model achieves competitive density estimation and orders of magnitude speedup - O(log N) sampling instead of O(N) - enabling the practical ","authors_text":"A\\\"aron van den Oord, Dan Belov, Nal Kalchbrenner, Nando de Freitas, Scott Reed, Sergio G\\'omez Colmenarejo, Ziyu Wang","cross_cats":["cs.NE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-10T12:58:23Z","title":"Parallel Multiscale Autoregressive Density Estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.03664","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":"sha256:5fca81860a4eccc130dfb8ce87021b7daba3b4063dd22f8f1642bb63d2e1c460","target":"record","created_at":"2026-05-18T00:48:58Z","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":"4bbcfb57b66fa9eee9c0f7e0aeddebd43d1afad72b64333dabc500b6a791f8fd","cross_cats_sorted":["cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-10T12:58:23Z","title_canon_sha256":"8583877b164b0bcb16ec090ac89e02fe1839e3fe62a45c57865c33637d19259f"},"schema_version":"1.0","source":{"id":"1703.03664","kind":"arxiv","version":1}},"canonical_sha256":"85f0e8fe382718c7fbd88542d786f17f8cdf819486584f62eced73a8ab3b9a85","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"85f0e8fe382718c7fbd88542d786f17f8cdf819486584f62eced73a8ab3b9a85","first_computed_at":"2026-05-18T00:48:58.399296Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:48:58.399296Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"W9g8rP6c2zXftwRTaSwjhj7wsJpM6nNRo4aoxCDmW3tCIr+aEKa1ga4hLnjhSQf+vyWOqdL1JZ0kK6P7Y1TJCA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:48:58.399964Z","signed_message":"canonical_sha256_bytes"},"source_id":"1703.03664","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5fca81860a4eccc130dfb8ce87021b7daba3b4063dd22f8f1642bb63d2e1c460","sha256:9503d68e27bee06e3123bcfe44c218b532afccbaff841236dfa1f81a8054e556"],"state_sha256":"06fdc2eb93c3f08653109d92250ed2d58e172db6def833c15d6d9817ab715ecf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"L2IKxA7wuPj8nCHkSR5D8nWqNiGe0bLElCdVXbFcDivMmqJxVliUkrJclxRvOQw2B3PlNPpoThoBT+iazZwwAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-18T20:08:34.477675Z","bundle_sha256":"19197ac2b95d1e00129fd9fbfb28ee016d8cdd81acc1dc4eb563248f9f844487"}}