{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:SCUS3UIO52AHL2CC3QEZLVEQYE","short_pith_number":"pith:SCUS3UIO","canonical_record":{"source":{"id":"1711.10046","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-11-27T23:45:02Z","cross_cats_sorted":["cs.IR","cs.LG"],"title_canon_sha256":"768fa6e8d69b8d9cc5f6121ec9a088358f7c2addc66928d563f81640ce62574f","abstract_canon_sha256":"32d4e68147a5ccbf9afe1f7309e0ca40f9f08ac8906842282edfcc3897428dad"},"schema_version":"1.0"},"canonical_sha256":"90a92dd10eee8075e842dc0995d490c121e743c2bcf9cc1bb7e67c0cf75b15d4","source":{"kind":"arxiv","id":"1711.10046","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.10046","created_at":"2026-05-18T00:29:23Z"},{"alias_kind":"arxiv_version","alias_value":"1711.10046v1","created_at":"2026-05-18T00:29:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.10046","created_at":"2026-05-18T00:29:23Z"},{"alias_kind":"pith_short_12","alias_value":"SCUS3UIO52AH","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_16","alias_value":"SCUS3UIO52AHL2CC","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_8","alias_value":"SCUS3UIO","created_at":"2026-05-18T12:31:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:SCUS3UIO52AHL2CC3QEZLVEQYE","target":"record","payload":{"canonical_record":{"source":{"id":"1711.10046","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-11-27T23:45:02Z","cross_cats_sorted":["cs.IR","cs.LG"],"title_canon_sha256":"768fa6e8d69b8d9cc5f6121ec9a088358f7c2addc66928d563f81640ce62574f","abstract_canon_sha256":"32d4e68147a5ccbf9afe1f7309e0ca40f9f08ac8906842282edfcc3897428dad"},"schema_version":"1.0"},"canonical_sha256":"90a92dd10eee8075e842dc0995d490c121e743c2bcf9cc1bb7e67c0cf75b15d4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:29:23.522085Z","signature_b64":"RjckRP9dwBT0xD1i3QRogH/AsYvdBo7wJ83ZvKqpmITX2PSWRlNZdtqN0OLFhBNj3H8z1t+Klam1HTCtDkOcBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"90a92dd10eee8075e842dc0995d490c121e743c2bcf9cc1bb7e67c0cf75b15d4","last_reissued_at":"2026-05-18T00:29:23.521350Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:29:23.521350Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1711.10046","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":"2026-05-18T00:29:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ENIabws6HhexI0h+OSQV4iWwYoQ4as62kP8Cs7OI3KzSuvMXsWQz7+UsyZ4tsGGZDEkEj03mqqmKh8S5HiTUCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T20:14:52.207417Z"},"content_sha256":"e28823a6d5fc331ad596f36a662f09d0d29667cddac6331bf08e5016108544e9","schema_version":"1.0","event_id":"sha256:e28823a6d5fc331ad596f36a662f09d0d29667cddac6331bf08e5016108544e9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:SCUS3UIO52AHL2CC3QEZLVEQYE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Recurrent Generative Adversarial Networks for Proximal Learning and Automated Compressive Image Recovery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR","cs.LG"],"primary_cat":"cs.AI","authors_text":"David Donoho, Hatef Monajemi, John Pauly, Morteza Mardani, Shreyas Vasanawala, Vardan Papyan","submitted_at":"2017-11-27T23:45:02Z","abstract_excerpt":"Recovering images from undersampled linear measurements typically leads to an ill-posed linear inverse problem, that asks for proper statistical priors. Building effective priors is however challenged by the low train and test overhead dictated by real-time tasks; and the need for retrieving visually \"plausible\" and physically \"feasible\" images with minimal hallucination. To cope with these challenges, we design a cascaded network architecture that unrolls the proximal gradient iterations by permeating benefits from generative residual networks (ResNet) to modeling the proximal operator. A mix"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.10046","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":"2026-05-18T00:29:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"beC+MbPXCqAx7KjFprb+fT3HCrGTVxuZAjI1+VxlaC0uOuHdXKpAaVFyZ2VByR2Huj4x8TlcZRg/HdZtqOz8DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T20:14:52.207864Z"},"content_sha256":"8a854a852c9fcce54dbc6426d21ecfe86ba572298cc227e8edd4f79130c0aa45","schema_version":"1.0","event_id":"sha256:8a854a852c9fcce54dbc6426d21ecfe86ba572298cc227e8edd4f79130c0aa45"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SCUS3UIO52AHL2CC3QEZLVEQYE/bundle.json","state_url":"https://pith.science/pith/SCUS3UIO52AHL2CC3QEZLVEQYE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SCUS3UIO52AHL2CC3QEZLVEQYE/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":"2026-05-27T20:14:52Z","links":{"resolver":"https://pith.science/pith/SCUS3UIO52AHL2CC3QEZLVEQYE","bundle":"https://pith.science/pith/SCUS3UIO52AHL2CC3QEZLVEQYE/bundle.json","state":"https://pith.science/pith/SCUS3UIO52AHL2CC3QEZLVEQYE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SCUS3UIO52AHL2CC3QEZLVEQYE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:SCUS3UIO52AHL2CC3QEZLVEQYE","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":"32d4e68147a5ccbf9afe1f7309e0ca40f9f08ac8906842282edfcc3897428dad","cross_cats_sorted":["cs.IR","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-11-27T23:45:02Z","title_canon_sha256":"768fa6e8d69b8d9cc5f6121ec9a088358f7c2addc66928d563f81640ce62574f"},"schema_version":"1.0","source":{"id":"1711.10046","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.10046","created_at":"2026-05-18T00:29:23Z"},{"alias_kind":"arxiv_version","alias_value":"1711.10046v1","created_at":"2026-05-18T00:29:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.10046","created_at":"2026-05-18T00:29:23Z"},{"alias_kind":"pith_short_12","alias_value":"SCUS3UIO52AH","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_16","alias_value":"SCUS3UIO52AHL2CC","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_8","alias_value":"SCUS3UIO","created_at":"2026-05-18T12:31:43Z"}],"graph_snapshots":[{"event_id":"sha256:8a854a852c9fcce54dbc6426d21ecfe86ba572298cc227e8edd4f79130c0aa45","target":"graph","created_at":"2026-05-18T00:29:23Z","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"},"paper":{"abstract_excerpt":"Recovering images from undersampled linear measurements typically leads to an ill-posed linear inverse problem, that asks for proper statistical priors. Building effective priors is however challenged by the low train and test overhead dictated by real-time tasks; and the need for retrieving visually \"plausible\" and physically \"feasible\" images with minimal hallucination. To cope with these challenges, we design a cascaded network architecture that unrolls the proximal gradient iterations by permeating benefits from generative residual networks (ResNet) to modeling the proximal operator. A mix","authors_text":"David Donoho, Hatef Monajemi, John Pauly, Morteza Mardani, Shreyas Vasanawala, Vardan Papyan","cross_cats":["cs.IR","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-11-27T23:45:02Z","title":"Recurrent Generative Adversarial Networks for Proximal Learning and Automated Compressive Image Recovery"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.10046","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:e28823a6d5fc331ad596f36a662f09d0d29667cddac6331bf08e5016108544e9","target":"record","created_at":"2026-05-18T00:29:23Z","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":"32d4e68147a5ccbf9afe1f7309e0ca40f9f08ac8906842282edfcc3897428dad","cross_cats_sorted":["cs.IR","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-11-27T23:45:02Z","title_canon_sha256":"768fa6e8d69b8d9cc5f6121ec9a088358f7c2addc66928d563f81640ce62574f"},"schema_version":"1.0","source":{"id":"1711.10046","kind":"arxiv","version":1}},"canonical_sha256":"90a92dd10eee8075e842dc0995d490c121e743c2bcf9cc1bb7e67c0cf75b15d4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"90a92dd10eee8075e842dc0995d490c121e743c2bcf9cc1bb7e67c0cf75b15d4","first_computed_at":"2026-05-18T00:29:23.521350Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:29:23.521350Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RjckRP9dwBT0xD1i3QRogH/AsYvdBo7wJ83ZvKqpmITX2PSWRlNZdtqN0OLFhBNj3H8z1t+Klam1HTCtDkOcBA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:29:23.522085Z","signed_message":"canonical_sha256_bytes"},"source_id":"1711.10046","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e28823a6d5fc331ad596f36a662f09d0d29667cddac6331bf08e5016108544e9","sha256:8a854a852c9fcce54dbc6426d21ecfe86ba572298cc227e8edd4f79130c0aa45"],"state_sha256":"6cd108d7457b3327ed8f80f6dc2cc2bb2d0b3e2adfa4c3449a8a01941d875e84"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"P1s36tDLeES0eU8a96fofpUQISrd+OV4ltjCbrzgMnHBsVLu7Pvd11Ju/WL2TYAjBB+Jx/WdhjY1IfGSqC2UBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T20:14:52.210275Z","bundle_sha256":"a878cb5579db5bf847b6f46378199fb3c75ca24a28494e1430f92ea48cca82f6"}}