{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:I7MXUFSKWVGIQQCL2SZX3ZLT22","short_pith_number":"pith:I7MXUFSK","schema_version":"1.0","canonical_sha256":"47d97a164ab54c88404bd4b37de573d6a037dccd1b3081361fac0cf1941d1719","source":{"kind":"arxiv","id":"2506.05391","version":2},"attestation_state":"computed","paper":{"title":"Enhancing Neural Autoregressive Distribution Estimators for Image Reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG","stat.AP"],"primary_cat":"eess.IV","authors_text":"Ambrose Emmett-Iwaniw, Nathan Kirk","submitted_at":"2025-06-03T18:44:54Z","abstract_excerpt":"Autoregressive models are often employed to learn distributions of image data by decomposing the $D$-dimensional density function into a product of one-dimensional conditional distributions. Each conditional depends on preceding variables (pixels, in the case of image data), making the order in which variables are processed fundamental to the model performance. In this paper, we study the problem of observing a small subset of image pixels (referred to as a pixel patch) to predict the unobserved parts of the image. As our prediction mechanism, we propose a generalized version of the convolutio"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2506.05391","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2025-06-03T18:44:54Z","cross_cats_sorted":["cs.CV","cs.LG","stat.AP"],"title_canon_sha256":"3fbdc8df91b8afb553dfbe34e56f7533e49d822ee8caa0a6835e8f5fef35feff","abstract_canon_sha256":"466bf93bcccf9213bd6791f3aab39b5c842e692b3770201f387ec5ba4daad93b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:32:30.243084Z","signature_b64":"q59kRu7nfN5iqjgTAOTiVaUk4Tls7HpD7IJzIuDdBCbNwtTsLZPqoADqS1r4r7x6Hcs5e6Wo1dROula/OsHzBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"47d97a164ab54c88404bd4b37de573d6a037dccd1b3081361fac0cf1941d1719","last_reissued_at":"2026-07-05T11:32:30.242600Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:32:30.242600Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhancing Neural Autoregressive Distribution Estimators for Image Reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG","stat.AP"],"primary_cat":"eess.IV","authors_text":"Ambrose Emmett-Iwaniw, Nathan Kirk","submitted_at":"2025-06-03T18:44:54Z","abstract_excerpt":"Autoregressive models are often employed to learn distributions of image data by decomposing the $D$-dimensional density function into a product of one-dimensional conditional distributions. Each conditional depends on preceding variables (pixels, in the case of image data), making the order in which variables are processed fundamental to the model performance. In this paper, we study the problem of observing a small subset of image pixels (referred to as a pixel patch) to predict the unobserved parts of the image. As our prediction mechanism, we propose a generalized version of the convolutio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.05391","kind":"arxiv","version":2},"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/2506.05391/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2506.05391","created_at":"2026-07-05T11:32:30.242667+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.05391v2","created_at":"2026-07-05T11:32:30.242667+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.05391","created_at":"2026-07-05T11:32:30.242667+00:00"},{"alias_kind":"pith_short_12","alias_value":"I7MXUFSKWVGI","created_at":"2026-07-05T11:32:30.242667+00:00"},{"alias_kind":"pith_short_16","alias_value":"I7MXUFSKWVGIQQCL","created_at":"2026-07-05T11:32:30.242667+00:00"},{"alias_kind":"pith_short_8","alias_value":"I7MXUFSK","created_at":"2026-07-05T11:32:30.242667+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/I7MXUFSKWVGIQQCL2SZX3ZLT22","json":"https://pith.science/pith/I7MXUFSKWVGIQQCL2SZX3ZLT22.json","graph_json":"https://pith.science/api/pith-number/I7MXUFSKWVGIQQCL2SZX3ZLT22/graph.json","events_json":"https://pith.science/api/pith-number/I7MXUFSKWVGIQQCL2SZX3ZLT22/events.json","paper":"https://pith.science/paper/I7MXUFSK"},"agent_actions":{"view_html":"https://pith.science/pith/I7MXUFSKWVGIQQCL2SZX3ZLT22","download_json":"https://pith.science/pith/I7MXUFSKWVGIQQCL2SZX3ZLT22.json","view_paper":"https://pith.science/paper/I7MXUFSK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.05391&json=true","fetch_graph":"https://pith.science/api/pith-number/I7MXUFSKWVGIQQCL2SZX3ZLT22/graph.json","fetch_events":"https://pith.science/api/pith-number/I7MXUFSKWVGIQQCL2SZX3ZLT22/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I7MXUFSKWVGIQQCL2SZX3ZLT22/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I7MXUFSKWVGIQQCL2SZX3ZLT22/action/storage_attestation","attest_author":"https://pith.science/pith/I7MXUFSKWVGIQQCL2SZX3ZLT22/action/author_attestation","sign_citation":"https://pith.science/pith/I7MXUFSKWVGIQQCL2SZX3ZLT22/action/citation_signature","submit_replication":"https://pith.science/pith/I7MXUFSKWVGIQQCL2SZX3ZLT22/action/replication_record"}},"created_at":"2026-07-05T11:32:30.242667+00:00","updated_at":"2026-07-05T11:32:30.242667+00:00"}