{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:AJDVARO3BSCSCDFJYQJCVVZLKZ","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":"08c8830105994af615f4b3fcfa042c7ffe0b1d9b4124a3966c07a41def6995a3","cross_cats_sorted":["cs.LG","cs.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2018-02-28T21:23:22Z","title_canon_sha256":"12289e356e1ae4cd3263ed2b111cbebd156af617bfa243d8f609709a12b681c4"},"schema_version":"1.0","source":{"id":"1803.00092","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.00092","created_at":"2026-07-05T00:24:34Z"},{"alias_kind":"arxiv_version","alias_value":"1803.00092v3","created_at":"2026-07-05T00:24:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.00092","created_at":"2026-07-05T00:24:34Z"},{"alias_kind":"pith_short_12","alias_value":"AJDVARO3BSCS","created_at":"2026-07-05T00:24:34Z"},{"alias_kind":"pith_short_16","alias_value":"AJDVARO3BSCSCDFJ","created_at":"2026-07-05T00:24:34Z"},{"alias_kind":"pith_short_8","alias_value":"AJDVARO3","created_at":"2026-07-05T00:24:34Z"}],"graph_snapshots":[{"event_id":"sha256:82fba03e7ead1906ef232f2a85b43769306a11efe9c24dbebd0fefddfb766cc7","target":"graph","created_at":"2026-07-05T00:24:34Z","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/1803.00092/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recovering a function or high-dimensional parameter vector from indirect measurements is a central task in various scientific areas. Several methods for solving such inverse problems are well developed and well understood. Recently, novel algorithms using deep learning and neural networks for inverse problems appeared. While still in their infancy, these techniques show astonishing performance for applications like low-dose CT or various sparse data problems. However, there are few theoretical results for deep learning in inverse problems. In this paper, we establish a complete convergence ana","authors_text":"Housen Li, Johannes Schwab, Markus Haltmeier, Stephan Antholzer","cross_cats":["cs.LG","cs.NA"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2018-02-28T21:23:22Z","title":"NETT: Solving Inverse Problems with Deep Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.00092","kind":"arxiv","version":3},"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":"sha256:696eaabb841946582cac87af8e33e41dec52789c8a32d0f8ca3fb6db75786d37","target":"record","created_at":"2026-07-05T00:24:34Z","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":"08c8830105994af615f4b3fcfa042c7ffe0b1d9b4124a3966c07a41def6995a3","cross_cats_sorted":["cs.LG","cs.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2018-02-28T21:23:22Z","title_canon_sha256":"12289e356e1ae4cd3263ed2b111cbebd156af617bfa243d8f609709a12b681c4"},"schema_version":"1.0","source":{"id":"1803.00092","kind":"arxiv","version":3}},"canonical_sha256":"02475045db0c85210ca9c4122ad72b564fb53034022e01f66aecb72b581d1df2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"02475045db0c85210ca9c4122ad72b564fb53034022e01f66aecb72b581d1df2","first_computed_at":"2026-07-05T00:24:34.753976Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:24:34.753976Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lInV1mOj0MzAd1lr2g++XvLEH2LR2qfWlzdmNTCD/vfaLJ39A9yg14+WY8tOSrvQGP7AtGEEP12/E/jKYg/wAg==","signature_status":"signed_v1","signed_at":"2026-07-05T00:24:34.754339Z","signed_message":"canonical_sha256_bytes"},"source_id":"1803.00092","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:696eaabb841946582cac87af8e33e41dec52789c8a32d0f8ca3fb6db75786d37","sha256:82fba03e7ead1906ef232f2a85b43769306a11efe9c24dbebd0fefddfb766cc7"],"state_sha256":"2b3dca12f0847f27d4384a99067cb5ddf5073dc18b3f09493b2eed9087476228"}