{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:5L3P5K3YCWNBMTXK5BSNU5BEFE","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":"801d9608861537a0cd8f8797d4c9c6ad2fef1787ab3b7094d3b48acb2f5fc6bc","cross_cats_sorted":["cs.NA","math.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-14T13:47:05Z","title_canon_sha256":"c6e534d0f183a898b387bdac77b2181d5e3c2c3db089e1d27723cb59a7512f64"},"schema_version":"1.0","source":{"id":"2307.07344","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.07344","created_at":"2026-07-05T08:38:33Z"},{"alias_kind":"arxiv_version","alias_value":"2307.07344v2","created_at":"2026-07-05T08:38:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.07344","created_at":"2026-07-05T08:38:33Z"},{"alias_kind":"pith_short_12","alias_value":"5L3P5K3YCWNB","created_at":"2026-07-05T08:38:33Z"},{"alias_kind":"pith_short_16","alias_value":"5L3P5K3YCWNBMTXK","created_at":"2026-07-05T08:38:33Z"},{"alias_kind":"pith_short_8","alias_value":"5L3P5K3Y","created_at":"2026-07-05T08:38:33Z"}],"graph_snapshots":[{"event_id":"sha256:73f858e61d78fb4ffd9a1a81d002b2e6d3a419b5c57b6e010a29d4c6f3b8813e","target":"graph","created_at":"2026-07-05T08:38:33Z","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/2307.07344/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Traditional image processing methods employing partial differential equations (PDEs) offer a multitude of meaningful regularizers, along with valuable theoretical foundations for a wide range of image-related tasks. This makes their integration into neural networks a promising avenue. In this paper, we introduce a novel regularization approach inspired by the reverse process of PDE-based evolution models. Specifically, we propose inverse evolution layers (IELs), which serve as bad property amplifiers to penalize neural networks of which outputs have undesired characteristics. Using IELs, one c","authors_text":"Carola-Bibiane Sch\\\"onlieb, Chao Li, Chaoyu Liu, Zhonghua Qiao","cross_cats":["cs.NA","math.NA"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-14T13:47:05Z","title":"Inverse Evolution Layers: Physics-informed Regularizers for Deep Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.07344","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:c9a613090f6a2b51623ac038c39a5d4fe5c6029c12cae1859372434f5d5aa3e9","target":"record","created_at":"2026-07-05T08:38:33Z","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":"801d9608861537a0cd8f8797d4c9c6ad2fef1787ab3b7094d3b48acb2f5fc6bc","cross_cats_sorted":["cs.NA","math.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-14T13:47:05Z","title_canon_sha256":"c6e534d0f183a898b387bdac77b2181d5e3c2c3db089e1d27723cb59a7512f64"},"schema_version":"1.0","source":{"id":"2307.07344","kind":"arxiv","version":2}},"canonical_sha256":"eaf6feab78159a164eeae864da74242935a33c3223e4ed5eae809088811649bf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"eaf6feab78159a164eeae864da74242935a33c3223e4ed5eae809088811649bf","first_computed_at":"2026-07-05T08:38:33.506913Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:38:33.506913Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BGTNIgJ72YC+3TV/oBcNBK0MaHo1pd68WD9gtOqiAukLjSLCoUTfx9HEiG7zG2kJXsQiJSNIq3TnFOpqbVlkDg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:38:33.508018Z","signed_message":"canonical_sha256_bytes"},"source_id":"2307.07344","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c9a613090f6a2b51623ac038c39a5d4fe5c6029c12cae1859372434f5d5aa3e9","sha256:73f858e61d78fb4ffd9a1a81d002b2e6d3a419b5c57b6e010a29d4c6f3b8813e"],"state_sha256":"84f4306c8b735fda2075806bdb4627c96f507319b9f9a70f26b349d183afb9f7"}