{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:FE7EUSG3VG3HCBD66434IQFCG4","short_pith_number":"pith:FE7EUSG3","schema_version":"1.0","canonical_sha256":"293e4a48dba9b671047ef737c440a2371e04d0484984a410ba95fcf0ec4fc2ae","source":{"kind":"arxiv","id":"2311.16833","version":1},"attestation_state":"computed","paper":{"title":"1-Lipschitz Layers Compared: Memory, Speed, and Certifiable Robustness","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CV","cs.NE"],"primary_cat":"cs.LG","authors_text":"Bernd Prach, Christoph H. Lampert, Fabio Brau, Giorgio Buttazzo","submitted_at":"2023-11-28T14:50:50Z","abstract_excerpt":"The robustness of neural networks against input perturbations with bounded magnitude represents a serious concern in the deployment of deep learning models in safety-critical systems. Recently, the scientific community has focused on enhancing certifiable robustness guarantees by crafting 1-Lipschitz neural networks that leverage Lipschitz bounded dense and convolutional layers. Although different methods have been proposed in the literature to achieve this goal, understanding the performance of such methods is not straightforward, since different metrics can be relevant (e.g., training time, "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2311.16833","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-11-28T14:50:50Z","cross_cats_sorted":["cs.CV","cs.NE"],"title_canon_sha256":"de9559ac17ad0911fc1b1873c77c15488b21b819fbc7229f215b8750b78ab133","abstract_canon_sha256":"64ddd3f5ba273abe4ea63c4b372b211fccf1ed9d695a25464735ad8120d0655d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:17:47.159350Z","signature_b64":"1XmpN9b473SBrJgnd5lDzAC4wCx+IcByaQSJ69i6vc2BTEGz0mQetwg27ToPDVmSHJQwp8Je7DsZa2bT3BcyAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"293e4a48dba9b671047ef737c440a2371e04d0484984a410ba95fcf0ec4fc2ae","last_reissued_at":"2026-07-05T07:17:47.158898Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:17:47.158898Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"1-Lipschitz Layers Compared: Memory, Speed, and Certifiable Robustness","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CV","cs.NE"],"primary_cat":"cs.LG","authors_text":"Bernd Prach, Christoph H. Lampert, Fabio Brau, Giorgio Buttazzo","submitted_at":"2023-11-28T14:50:50Z","abstract_excerpt":"The robustness of neural networks against input perturbations with bounded magnitude represents a serious concern in the deployment of deep learning models in safety-critical systems. Recently, the scientific community has focused on enhancing certifiable robustness guarantees by crafting 1-Lipschitz neural networks that leverage Lipschitz bounded dense and convolutional layers. Although different methods have been proposed in the literature to achieve this goal, understanding the performance of such methods is not straightforward, since different metrics can be relevant (e.g., training time, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.16833","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2311.16833/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2311.16833","created_at":"2026-07-05T07:17:47.158964+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.16833v1","created_at":"2026-07-05T07:17:47.158964+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.16833","created_at":"2026-07-05T07:17:47.158964+00:00"},{"alias_kind":"pith_short_12","alias_value":"FE7EUSG3VG3H","created_at":"2026-07-05T07:17:47.158964+00:00"},{"alias_kind":"pith_short_16","alias_value":"FE7EUSG3VG3HCBD6","created_at":"2026-07-05T07:17:47.158964+00:00"},{"alias_kind":"pith_short_8","alias_value":"FE7EUSG3","created_at":"2026-07-05T07:17:47.158964+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.15174","citing_title":"Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss","ref_index":45,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FE7EUSG3VG3HCBD66434IQFCG4","json":"https://pith.science/pith/FE7EUSG3VG3HCBD66434IQFCG4.json","graph_json":"https://pith.science/api/pith-number/FE7EUSG3VG3HCBD66434IQFCG4/graph.json","events_json":"https://pith.science/api/pith-number/FE7EUSG3VG3HCBD66434IQFCG4/events.json","paper":"https://pith.science/paper/FE7EUSG3"},"agent_actions":{"view_html":"https://pith.science/pith/FE7EUSG3VG3HCBD66434IQFCG4","download_json":"https://pith.science/pith/FE7EUSG3VG3HCBD66434IQFCG4.json","view_paper":"https://pith.science/paper/FE7EUSG3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.16833&json=true","fetch_graph":"https://pith.science/api/pith-number/FE7EUSG3VG3HCBD66434IQFCG4/graph.json","fetch_events":"https://pith.science/api/pith-number/FE7EUSG3VG3HCBD66434IQFCG4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FE7EUSG3VG3HCBD66434IQFCG4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FE7EUSG3VG3HCBD66434IQFCG4/action/storage_attestation","attest_author":"https://pith.science/pith/FE7EUSG3VG3HCBD66434IQFCG4/action/author_attestation","sign_citation":"https://pith.science/pith/FE7EUSG3VG3HCBD66434IQFCG4/action/citation_signature","submit_replication":"https://pith.science/pith/FE7EUSG3VG3HCBD66434IQFCG4/action/replication_record"}},"created_at":"2026-07-05T07:17:47.158964+00:00","updated_at":"2026-07-05T07:17:47.158964+00:00"}