{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:OOJZX2AXURT6HLCWPDRN2JFLVW","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":"49b18322b0ae5bf7b5b6e6da1068693dde9b990bb0d315dd4bd4d582d4dc7db3","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-08-06T18:58:35Z","title_canon_sha256":"56ce7696c72dbb0042290d466d943eb96f7fa1ab087a332fe682d77dc15589d7"},"schema_version":"1.0","source":{"id":"2008.02839","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2008.02839","created_at":"2026-07-05T02:18:54Z"},{"alias_kind":"arxiv_version","alias_value":"2008.02839v2","created_at":"2026-07-05T02:18:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2008.02839","created_at":"2026-07-05T02:18:54Z"},{"alias_kind":"pith_short_12","alias_value":"OOJZX2AXURT6","created_at":"2026-07-05T02:18:54Z"},{"alias_kind":"pith_short_16","alias_value":"OOJZX2AXURT6HLCW","created_at":"2026-07-05T02:18:54Z"},{"alias_kind":"pith_short_8","alias_value":"OOJZX2AX","created_at":"2026-07-05T02:18:54Z"}],"graph_snapshots":[{"event_id":"sha256:f3052a4833d88e34d14c9386435422123c9b5c5ddb67e30fc5ee7efc346bb9b1","target":"graph","created_at":"2026-07-05T02:18:54Z","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/2008.02839/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We consider the variational reconstruction framework for inverse problems and propose to learn a data-adaptive input-convex neural network (ICNN) as the regularization functional. The ICNN-based convex regularizer is trained adversarially to discern ground-truth images from unregularized reconstructions. Convexity of the regularizer is desirable since (i) one can establish analytical convergence guarantees for the corresponding variational reconstruction problem and (ii) devise efficient and provable algorithms for reconstruction. In particular, we show that the optimal solution to the variati","authors_text":"Carola-Bibiane Sch\\\"onlieb, Ozan \\\"Oktem, Sebastian Lunz, S\\\"oren Dittmer, Subhadip Mukherjee, Zakhar Shumaylov","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-08-06T18:58:35Z","title":"Learned convex regularizers for inverse problems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2008.02839","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:e1aabfa72c575a27ab7bcb9cf95b6b0131c132c9e62b349c1a2ebb616de25be4","target":"record","created_at":"2026-07-05T02:18:54Z","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":"49b18322b0ae5bf7b5b6e6da1068693dde9b990bb0d315dd4bd4d582d4dc7db3","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-08-06T18:58:35Z","title_canon_sha256":"56ce7696c72dbb0042290d466d943eb96f7fa1ab087a332fe682d77dc15589d7"},"schema_version":"1.0","source":{"id":"2008.02839","kind":"arxiv","version":2}},"canonical_sha256":"73939be817a467e3ac5678e2dd24abadb181273531731d5e6e8819dc91c19da9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"73939be817a467e3ac5678e2dd24abadb181273531731d5e6e8819dc91c19da9","first_computed_at":"2026-07-05T02:18:54.571304Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:18:54.571304Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uiuTZotJ7LdpB5e3Va0kWt19zrYuZaHxyEcv13Ar7yP1XYwK/LnJe1DnPYR8WrIouzh/J6mEp0RdME5dSHbMDA==","signature_status":"signed_v1","signed_at":"2026-07-05T02:18:54.571702Z","signed_message":"canonical_sha256_bytes"},"source_id":"2008.02839","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e1aabfa72c575a27ab7bcb9cf95b6b0131c132c9e62b349c1a2ebb616de25be4","sha256:f3052a4833d88e34d14c9386435422123c9b5c5ddb67e30fc5ee7efc346bb9b1"],"state_sha256":"c91cd5a028b0b79167c6bdcba7b214fc37f88fa2d007c3df78ae551ad83f6a65"}