{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:YGDO3SKLFAFUKY6HBF6ERGPYYH","short_pith_number":"pith:YGDO3SKL","canonical_record":{"source":{"id":"1901.03707","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-01-13T17:44:22Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"3b100ca85ad013ca646345f75fe0b1ef6fc8db93e4176ac5e732fbb56d8b2ae1","abstract_canon_sha256":"dc456e217155133c01221bf2e407de16fe242f72cc8bc8d56e8ad8a6c0ca666c"},"schema_version":"1.0"},"canonical_sha256":"c186edc94b280b4563c7097c4899f8c1f9a63fa4b4d7f4b527cbb0e6aae80d42","source":{"kind":"arxiv","id":"1901.03707","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.03707","created_at":"2026-05-17T23:44:21Z"},{"alias_kind":"arxiv_version","alias_value":"1901.03707v2","created_at":"2026-05-17T23:44:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.03707","created_at":"2026-05-17T23:44:21Z"},{"alias_kind":"pith_short_12","alias_value":"YGDO3SKLFAFU","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"YGDO3SKLFAFUKY6H","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"YGDO3SKL","created_at":"2026-05-18T12:33:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:YGDO3SKLFAFUKY6HBF6ERGPYYH","target":"record","payload":{"canonical_record":{"source":{"id":"1901.03707","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-01-13T17:44:22Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"3b100ca85ad013ca646345f75fe0b1ef6fc8db93e4176ac5e732fbb56d8b2ae1","abstract_canon_sha256":"dc456e217155133c01221bf2e407de16fe242f72cc8bc8d56e8ad8a6c0ca666c"},"schema_version":"1.0"},"canonical_sha256":"c186edc94b280b4563c7097c4899f8c1f9a63fa4b4d7f4b527cbb0e6aae80d42","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:21.852414Z","signature_b64":"O2GbrcrXMqHypXeBkiHpaWfDv/bW3eB86SaJVZSb8ZlVL0i0RpcTsr5UzIcJHgEaQZrwyBFWMwAmz4OLvvrTDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c186edc94b280b4563c7097c4899f8c1f9a63fa4b4d7f4b527cbb0e6aae80d42","last_reissued_at":"2026-05-17T23:44:21.851706Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:21.851706Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1901.03707","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:44:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5EtZXG8TFasZk1+6g8hlyi3Y86QQzlS8W0pkQg36KAdhWGkr16S2kzWY/Cp4uVEsOMXYdXFQcDBvgB8lYX0yAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T14:30:26.339830Z"},"content_sha256":"a06246a3e191a73fc0638dc774bf7bc13e2915b88a9afd5ebe48fba6f4d793d8","schema_version":"1.0","event_id":"sha256:a06246a3e191a73fc0638dc774bf7bc13e2915b88a9afd5ebe48fba6f4d793d8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:YGDO3SKLFAFUKY6HBF6ERGPYYH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Neumann Networks for Inverse Problems in Imaging","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Davis Gilton, Greg Ongie, Rebecca Willett","submitted_at":"2019-01-13T17:44:22Z","abstract_excerpt":"Many challenging image processing tasks can be described by an ill-posed linear inverse problem: deblurring, deconvolution, inpainting, compressed sensing, and superresolution all lie in this framework. Traditional inverse problem solvers minimize a cost function consisting of a data-fit term, which measures how well an image matches the observations, and a regularizer, which reflects prior knowledge and promotes images with desirable properties like smoothness. Recent advances in machine learning and image processing have illustrated that it is often possible to learn a regularizer from train"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.03707","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":""},"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":"2026-05-17T23:44:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UPeync5edX6thR/JwRhnzID3w+EW9CnOFFO5R13yicvLbz5FmGiaI4RJ0tmBzhyRPipA97YqOEs3jel4WaprCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T14:30:26.348608Z"},"content_sha256":"3370ae76d92ce6c51a35e487ff85841364c678f702b786a5fbb6d6f197424f93","schema_version":"1.0","event_id":"sha256:3370ae76d92ce6c51a35e487ff85841364c678f702b786a5fbb6d6f197424f93"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YGDO3SKLFAFUKY6HBF6ERGPYYH/bundle.json","state_url":"https://pith.science/pith/YGDO3SKLFAFUKY6HBF6ERGPYYH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YGDO3SKLFAFUKY6HBF6ERGPYYH/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":"2026-08-11T14:30:26Z","links":{"resolver":"https://pith.science/pith/YGDO3SKLFAFUKY6HBF6ERGPYYH","bundle":"https://pith.science/pith/YGDO3SKLFAFUKY6HBF6ERGPYYH/bundle.json","state":"https://pith.science/pith/YGDO3SKLFAFUKY6HBF6ERGPYYH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YGDO3SKLFAFUKY6HBF6ERGPYYH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:YGDO3SKLFAFUKY6HBF6ERGPYYH","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":"dc456e217155133c01221bf2e407de16fe242f72cc8bc8d56e8ad8a6c0ca666c","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-01-13T17:44:22Z","title_canon_sha256":"3b100ca85ad013ca646345f75fe0b1ef6fc8db93e4176ac5e732fbb56d8b2ae1"},"schema_version":"1.0","source":{"id":"1901.03707","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.03707","created_at":"2026-05-17T23:44:21Z"},{"alias_kind":"arxiv_version","alias_value":"1901.03707v2","created_at":"2026-05-17T23:44:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.03707","created_at":"2026-05-17T23:44:21Z"},{"alias_kind":"pith_short_12","alias_value":"YGDO3SKLFAFU","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"YGDO3SKLFAFUKY6H","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"YGDO3SKL","created_at":"2026-05-18T12:33:33Z"}],"graph_snapshots":[{"event_id":"sha256:3370ae76d92ce6c51a35e487ff85841364c678f702b786a5fbb6d6f197424f93","target":"graph","created_at":"2026-05-17T23:44:21Z","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":"Many challenging image processing tasks can be described by an ill-posed linear inverse problem: deblurring, deconvolution, inpainting, compressed sensing, and superresolution all lie in this framework. Traditional inverse problem solvers minimize a cost function consisting of a data-fit term, which measures how well an image matches the observations, and a regularizer, which reflects prior knowledge and promotes images with desirable properties like smoothness. Recent advances in machine learning and image processing have illustrated that it is often possible to learn a regularizer from train","authors_text":"Davis Gilton, Greg Ongie, Rebecca Willett","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-01-13T17:44:22Z","title":"Neumann Networks for Inverse Problems in Imaging"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.03707","kind":"arxiv","version":2},"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:a06246a3e191a73fc0638dc774bf7bc13e2915b88a9afd5ebe48fba6f4d793d8","target":"record","created_at":"2026-05-17T23:44:21Z","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":"dc456e217155133c01221bf2e407de16fe242f72cc8bc8d56e8ad8a6c0ca666c","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-01-13T17:44:22Z","title_canon_sha256":"3b100ca85ad013ca646345f75fe0b1ef6fc8db93e4176ac5e732fbb56d8b2ae1"},"schema_version":"1.0","source":{"id":"1901.03707","kind":"arxiv","version":2}},"canonical_sha256":"c186edc94b280b4563c7097c4899f8c1f9a63fa4b4d7f4b527cbb0e6aae80d42","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c186edc94b280b4563c7097c4899f8c1f9a63fa4b4d7f4b527cbb0e6aae80d42","first_computed_at":"2026-05-17T23:44:21.851706Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:44:21.851706Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"O2GbrcrXMqHypXeBkiHpaWfDv/bW3eB86SaJVZSb8ZlVL0i0RpcTsr5UzIcJHgEaQZrwyBFWMwAmz4OLvvrTDQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:44:21.852414Z","signed_message":"canonical_sha256_bytes"},"source_id":"1901.03707","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a06246a3e191a73fc0638dc774bf7bc13e2915b88a9afd5ebe48fba6f4d793d8","sha256:3370ae76d92ce6c51a35e487ff85841364c678f702b786a5fbb6d6f197424f93"],"state_sha256":"d1c555c4a679f55bfdd1b5737641299756ba032b7f7795ddecec0c6b8b750698"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ciKqBDlnFD1xiQ2JakvI+sYQU50dtoZg9Dt+KLeC4TQu34S4l0l//ZthVYlBT1O5fE9ck7Xz9pVlVTOFipsHCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T14:30:26.354142Z","bundle_sha256":"532ff6dbad8cf90778709b6e4eb560c4d1be607a4abf11a370ff81b521b51668"}}