{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:R6UIIT24XVZBD4GIYTSNA3PGPZ","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":"062893a5b81ad727ce3b7a156e6ce7b8a974066518419f7cdfd35774fff61268","cross_cats_sorted":["math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2019-01-15T21:08:22Z","title_canon_sha256":"eb12751d174c1c41c0ad3f056308e75dd52d466f4fca7bdd625934360303e29e"},"schema_version":"1.0","source":{"id":"1901.05045","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.05045","created_at":"2026-07-05T00:26:33Z"},{"alias_kind":"arxiv_version","alias_value":"1901.05045v2","created_at":"2026-07-05T00:26:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.05045","created_at":"2026-07-05T00:26:33Z"},{"alias_kind":"pith_short_12","alias_value":"R6UIIT24XVZB","created_at":"2026-07-05T00:26:33Z"},{"alias_kind":"pith_short_16","alias_value":"R6UIIT24XVZBD4GI","created_at":"2026-07-05T00:26:33Z"},{"alias_kind":"pith_short_8","alias_value":"R6UIIT24","created_at":"2026-07-05T00:26:33Z"}],"graph_snapshots":[{"event_id":"sha256:cbfa1e739b35e56518976b646aec1a81a5dc4aa1ef8d821c0183761ccd638fcc","target":"graph","created_at":"2026-07-05T00:26:33Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1901.05045/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Compressed sensing (CS) is about recovering a structured signal from its under-determined linear measurements. Starting from sparsity, recovery methods have steadily moved towards more complex structures. Emerging machine learning tools such as generative functions that are based on neural networks are able to learn general complex structures from training data. This makes them potentially powerful tools for designing CS algorithms. Consider a desired class of signals $\\cal Q$, ${\\cal Q}\\subset{R}^n$, and a corresponding generative function $g:{\\cal U}^k\\rightarrow {R}^n$, ${\\cal U}\\subset {R}","authors_text":"Pei Peng, Shirin Jalali, Xin Yuan","cross_cats":["math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2019-01-15T21:08:22Z","title":"Solving inverse problems via auto-encoders"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.05045","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":"sha256:5190d4ec4b681b80537276a89d196cd8c922d8e8b4a556ac35b4d257a7f50b21","target":"record","created_at":"2026-07-05T00:26:33Z","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":"062893a5b81ad727ce3b7a156e6ce7b8a974066518419f7cdfd35774fff61268","cross_cats_sorted":["math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2019-01-15T21:08:22Z","title_canon_sha256":"eb12751d174c1c41c0ad3f056308e75dd52d466f4fca7bdd625934360303e29e"},"schema_version":"1.0","source":{"id":"1901.05045","kind":"arxiv","version":2}},"canonical_sha256":"8fa8844f5cbd7211f0c8c4e4d06de67e5f1b79815e7b5eb63b6c452c95183488","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8fa8844f5cbd7211f0c8c4e4d06de67e5f1b79815e7b5eb63b6c452c95183488","first_computed_at":"2026-07-05T00:26:33.125118Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:26:33.125118Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"e19dfywQcyE/RC0SFOvQnfpMByrpgtE1Z3qg/vChIAcRyXpcSmWruaRx6VbFVT2mDmxz5xDsBlnnwLPq0rHTAg==","signature_status":"signed_v1","signed_at":"2026-07-05T00:26:33.125699Z","signed_message":"canonical_sha256_bytes"},"source_id":"1901.05045","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5190d4ec4b681b80537276a89d196cd8c922d8e8b4a556ac35b4d257a7f50b21","sha256:cbfa1e739b35e56518976b646aec1a81a5dc4aa1ef8d821c0183761ccd638fcc"],"state_sha256":"5cade0e945f2c61d25c79e6065f39aae9d63781de73a23b7743d06b71cebea33"}