{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:MDVB6HUX5WGAW2I7XDHLFBZ6AS","short_pith_number":"pith:MDVB6HUX","schema_version":"1.0","canonical_sha256":"60ea1f1e97ed8c0b691fb8ceb2873e04a91075319688def2d9b680cfc168c214","source":{"kind":"arxiv","id":"1709.01841","version":3},"attestation_state":"computed","paper":{"title":"An inner-loop free solution to inverse problems using deep neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kai Fan, Katherine A. Heller, Lawrence Carin, Qi Wei","submitted_at":"2017-09-06T14:41:33Z","abstract_excerpt":"We propose a new method that uses deep learning techniques to accelerate the popular alternating direction method of multipliers (ADMM) solution for inverse problems. The ADMM updates consist of a proximity operator, a least squares regression that includes a big matrix inversion, and an explicit solution for updating the dual variables. Typically, inner loops are required to solve the first two sub-minimization problems due to the intractability of the prior and the matrix inversion. To avoid such drawbacks or limitations, we propose an inner-loop free update rule with two pre-trained deep co"},"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":"1709.01841","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-09-06T14:41:33Z","cross_cats_sorted":[],"title_canon_sha256":"a4d2861b22649f3e377c1f5f717ab7db628929a1338313e8255a9397464901f8","abstract_canon_sha256":"bba026b77408d60150419e7c65cc9b9c9ccf62828ccb3d2db099814c7abd8d94"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:30:33.925622Z","signature_b64":"UuYDs2BNf5iLyceFiTlsSx4Xi0RdBb7B50m4kT+7DUQhQIprJPqe2VVAbhZZjpllzFOTFj5StqxBBWrorUyEBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"60ea1f1e97ed8c0b691fb8ceb2873e04a91075319688def2d9b680cfc168c214","last_reissued_at":"2026-05-18T00:30:33.925006Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:30:33.925006Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An inner-loop free solution to inverse problems using deep neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kai Fan, Katherine A. Heller, Lawrence Carin, Qi Wei","submitted_at":"2017-09-06T14:41:33Z","abstract_excerpt":"We propose a new method that uses deep learning techniques to accelerate the popular alternating direction method of multipliers (ADMM) solution for inverse problems. The ADMM updates consist of a proximity operator, a least squares regression that includes a big matrix inversion, and an explicit solution for updating the dual variables. Typically, inner loops are required to solve the first two sub-minimization problems due to the intractability of the prior and the matrix inversion. To avoid such drawbacks or limitations, we propose an inner-loop free update rule with two pre-trained deep co"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.01841","kind":"arxiv","version":3},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1709.01841","created_at":"2026-05-18T00:30:33.925087+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.01841v3","created_at":"2026-05-18T00:30:33.925087+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.01841","created_at":"2026-05-18T00:30:33.925087+00:00"},{"alias_kind":"pith_short_12","alias_value":"MDVB6HUX5WGA","created_at":"2026-05-18T12:31:31.346846+00:00"},{"alias_kind":"pith_short_16","alias_value":"MDVB6HUX5WGAW2I7","created_at":"2026-05-18T12:31:31.346846+00:00"},{"alias_kind":"pith_short_8","alias_value":"MDVB6HUX","created_at":"2026-05-18T12:31:31.346846+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/MDVB6HUX5WGAW2I7XDHLFBZ6AS","json":"https://pith.science/pith/MDVB6HUX5WGAW2I7XDHLFBZ6AS.json","graph_json":"https://pith.science/api/pith-number/MDVB6HUX5WGAW2I7XDHLFBZ6AS/graph.json","events_json":"https://pith.science/api/pith-number/MDVB6HUX5WGAW2I7XDHLFBZ6AS/events.json","paper":"https://pith.science/paper/MDVB6HUX"},"agent_actions":{"view_html":"https://pith.science/pith/MDVB6HUX5WGAW2I7XDHLFBZ6AS","download_json":"https://pith.science/pith/MDVB6HUX5WGAW2I7XDHLFBZ6AS.json","view_paper":"https://pith.science/paper/MDVB6HUX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.01841&json=true","fetch_graph":"https://pith.science/api/pith-number/MDVB6HUX5WGAW2I7XDHLFBZ6AS/graph.json","fetch_events":"https://pith.science/api/pith-number/MDVB6HUX5WGAW2I7XDHLFBZ6AS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MDVB6HUX5WGAW2I7XDHLFBZ6AS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MDVB6HUX5WGAW2I7XDHLFBZ6AS/action/storage_attestation","attest_author":"https://pith.science/pith/MDVB6HUX5WGAW2I7XDHLFBZ6AS/action/author_attestation","sign_citation":"https://pith.science/pith/MDVB6HUX5WGAW2I7XDHLFBZ6AS/action/citation_signature","submit_replication":"https://pith.science/pith/MDVB6HUX5WGAW2I7XDHLFBZ6AS/action/replication_record"}},"created_at":"2026-05-18T00:30:33.925087+00:00","updated_at":"2026-05-18T00:30:33.925087+00:00"}