{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:T2EPD4EJEDSXKOX5AKW7LHT7BI","short_pith_number":"pith:T2EPD4EJ","schema_version":"1.0","canonical_sha256":"9e88f1f08920e5753afd02adf59e7f0a1b111ea2516db6d9eebc8b51195ed000","source":{"kind":"arxiv","id":"1207.3859","version":3},"attestation_state":"computed","paper":{"title":"Approximate Message Passing with Consistent Parameter Estimation and Applications to Sparse Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.IT"],"primary_cat":"cs.IT","authors_text":"Alyson K. Fletcher, Michael Unser, Sundeep Rangan, Ulugbek S. Kamilov","submitted_at":"2012-07-17T01:50:46Z","abstract_excerpt":"We consider the estimation of an i.i.d. (possibly non-Gaussian) vector $\\xbf \\in \\R^n$ from measurements $\\ybf \\in \\R^m$ obtained by a general cascade model consisting of a known linear transform followed by a probabilistic componentwise (possibly nonlinear) measurement channel. A novel method, called adaptive generalized approximate message passing (Adaptive GAMP), that enables joint learning of the statistics of the prior and measurement channel along with estimation of the unknown vector $\\xbf$ is presented. The proposed algorithm is a generalization of a recently-developed EM-GAMP that use"},"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":"1207.3859","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2012-07-17T01:50:46Z","cross_cats_sorted":["cs.LG","math.IT"],"title_canon_sha256":"b8179a106b850a6c4c7025fb28deae5105a99c9839b018693b5b8e747ea93a2e","abstract_canon_sha256":"292d5dc8d13174bc5d79e17b1eefea0ca8c4f1af58aa62dd6d9a61c79b9ddcf9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:39:28.340767Z","signature_b64":"RUWBn8xQwATbhQTB/LX7m0FwFGvtNPv2F3ioR6NZunL6Gb0pxeGa0xsfD0wRC5TlW43P2DnNSisUOPO7ziQ5Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9e88f1f08920e5753afd02adf59e7f0a1b111ea2516db6d9eebc8b51195ed000","last_reissued_at":"2026-05-18T03:39:28.339601Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:39:28.339601Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Approximate Message Passing with Consistent Parameter Estimation and Applications to Sparse Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.IT"],"primary_cat":"cs.IT","authors_text":"Alyson K. Fletcher, Michael Unser, Sundeep Rangan, Ulugbek S. Kamilov","submitted_at":"2012-07-17T01:50:46Z","abstract_excerpt":"We consider the estimation of an i.i.d. (possibly non-Gaussian) vector $\\xbf \\in \\R^n$ from measurements $\\ybf \\in \\R^m$ obtained by a general cascade model consisting of a known linear transform followed by a probabilistic componentwise (possibly nonlinear) measurement channel. A novel method, called adaptive generalized approximate message passing (Adaptive GAMP), that enables joint learning of the statistics of the prior and measurement channel along with estimation of the unknown vector $\\xbf$ is presented. The proposed algorithm is a generalization of a recently-developed EM-GAMP that use"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1207.3859","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":"1207.3859","created_at":"2026-05-18T03:39:28.340071+00:00"},{"alias_kind":"arxiv_version","alias_value":"1207.3859v3","created_at":"2026-05-18T03:39:28.340071+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1207.3859","created_at":"2026-05-18T03:39:28.340071+00:00"},{"alias_kind":"pith_short_12","alias_value":"T2EPD4EJEDSX","created_at":"2026-05-18T12:27:23.164592+00:00"},{"alias_kind":"pith_short_16","alias_value":"T2EPD4EJEDSXKOX5","created_at":"2026-05-18T12:27:23.164592+00:00"},{"alias_kind":"pith_short_8","alias_value":"T2EPD4EJ","created_at":"2026-05-18T12:27:23.164592+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/T2EPD4EJEDSXKOX5AKW7LHT7BI","json":"https://pith.science/pith/T2EPD4EJEDSXKOX5AKW7LHT7BI.json","graph_json":"https://pith.science/api/pith-number/T2EPD4EJEDSXKOX5AKW7LHT7BI/graph.json","events_json":"https://pith.science/api/pith-number/T2EPD4EJEDSXKOX5AKW7LHT7BI/events.json","paper":"https://pith.science/paper/T2EPD4EJ"},"agent_actions":{"view_html":"https://pith.science/pith/T2EPD4EJEDSXKOX5AKW7LHT7BI","download_json":"https://pith.science/pith/T2EPD4EJEDSXKOX5AKW7LHT7BI.json","view_paper":"https://pith.science/paper/T2EPD4EJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1207.3859&json=true","fetch_graph":"https://pith.science/api/pith-number/T2EPD4EJEDSXKOX5AKW7LHT7BI/graph.json","fetch_events":"https://pith.science/api/pith-number/T2EPD4EJEDSXKOX5AKW7LHT7BI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T2EPD4EJEDSXKOX5AKW7LHT7BI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T2EPD4EJEDSXKOX5AKW7LHT7BI/action/storage_attestation","attest_author":"https://pith.science/pith/T2EPD4EJEDSXKOX5AKW7LHT7BI/action/author_attestation","sign_citation":"https://pith.science/pith/T2EPD4EJEDSXKOX5AKW7LHT7BI/action/citation_signature","submit_replication":"https://pith.science/pith/T2EPD4EJEDSXKOX5AKW7LHT7BI/action/replication_record"}},"created_at":"2026-05-18T03:39:28.340071+00:00","updated_at":"2026-05-18T03:39:28.340071+00:00"}