{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:2DISIRF23OPHSEUXAK4PHLFCWT","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":"40201fd317a874a7e2e1cda9b74704e89e03c0024b1df9ac6d9425a31136b34b","cross_cats_sorted":["math.IT","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2019-03-01T16:25:55Z","title_canon_sha256":"9c71b6274d94104e857ba3d2ef1f5898124dd573e0d6903d939ea11686b7cbcb"},"schema_version":"1.0","source":{"id":"1903.01293","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.01293","created_at":"2026-05-17T23:52:09Z"},{"alias_kind":"arxiv_version","alias_value":"1903.01293v1","created_at":"2026-05-17T23:52:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.01293","created_at":"2026-05-17T23:52:09Z"},{"alias_kind":"pith_short_12","alias_value":"2DISIRF23OPH","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_16","alias_value":"2DISIRF23OPHSEUX","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_8","alias_value":"2DISIRF2","created_at":"2026-05-18T12:33:07Z"}],"graph_snapshots":[{"event_id":"sha256:f297cd5c959d973cd3e7d8e224facb3f64833508ef5a316e11553811c4ac59ce","target":"graph","created_at":"2026-05-17T23:52:09Z","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":"Deep generative priors are a powerful tool for reconstruction problems with complex data such as images and text. Inverse problems using such models require solving an inference problem of estimating the input and hidden units of the multi-layer network from its output. Maximum a priori (MAP) estimation is a widely-used inference method as it is straightforward to implement, and has been successful in practice. However, rigorous analysis of MAP inference in multi-layer networks is difficult. This work considers a recently-developed method, multi-layer vector approximate message passing (ML-VAM","authors_text":"Alyson K. Fletcher, Mojtaba Sahraee, Parthe Pandit, Sundeep Rangan","cross_cats":["math.IT","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2019-03-01T16:25:55Z","title":"Asymptotics of MAP Inference in Deep Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.01293","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:a27f020746888eb43889ecdda8b3a3c5db0ab8497ec20c6833847db2ab8967f7","target":"record","created_at":"2026-05-17T23:52:09Z","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":"40201fd317a874a7e2e1cda9b74704e89e03c0024b1df9ac6d9425a31136b34b","cross_cats_sorted":["math.IT","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2019-03-01T16:25:55Z","title_canon_sha256":"9c71b6274d94104e857ba3d2ef1f5898124dd573e0d6903d939ea11686b7cbcb"},"schema_version":"1.0","source":{"id":"1903.01293","kind":"arxiv","version":1}},"canonical_sha256":"d0d12444badb9e79129702b8f3aca2b4cc017f76ed2340d3848a32399a3b53af","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d0d12444badb9e79129702b8f3aca2b4cc017f76ed2340d3848a32399a3b53af","first_computed_at":"2026-05-17T23:52:09.176994Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:52:09.176994Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GdU+vKlkEFzYjxeEMpXwrIvA9rbbxu+QsasA68JoBrtFW04pI0wIk+vQhX6qqHXkGXpZ4gRhA/oFxSWfcB+qCA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:52:09.177556Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.01293","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a27f020746888eb43889ecdda8b3a3c5db0ab8497ec20c6833847db2ab8967f7","sha256:f297cd5c959d973cd3e7d8e224facb3f64833508ef5a316e11553811c4ac59ce"],"state_sha256":"ac2eaf5c00c02b97ddc61d4758c1e0443396b94ce5a84f558069e4894b31695a"}