{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:3DGIZWS7F6UZKIYMTTAXJOILSL","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":"face99cec17541bc86d15b3e2960cc6d6f265ecd4771e75918ec2a348fe1c533","cross_cats_sorted":["cs.CR","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-30T13:12:47Z","title_canon_sha256":"6c841c7bfbcb6bc7a03ca755b062a12f6a9d43e7c673a0b4958139e436d21cc8"},"schema_version":"1.0","source":{"id":"1810.12715","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.12715","created_at":"2026-07-05T00:00:23Z"},{"alias_kind":"arxiv_version","alias_value":"1810.12715v4","created_at":"2026-07-05T00:00:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.12715","created_at":"2026-07-05T00:00:23Z"},{"alias_kind":"pith_short_12","alias_value":"3DGIZWS7F6UZ","created_at":"2026-07-05T00:00:23Z"},{"alias_kind":"pith_short_16","alias_value":"3DGIZWS7F6UZKIYM","created_at":"2026-07-05T00:00:23Z"},{"alias_kind":"pith_short_8","alias_value":"3DGIZWS7","created_at":"2026-07-05T00:00:23Z"}],"graph_snapshots":[{"event_id":"sha256:828d3516e8067e9810e1f26299247e5b64656d16a62b903f78e85ce73673e9b6","target":"graph","created_at":"2026-07-05T00:00:23Z","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/1810.12715/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent work has shown that it is possible to train deep neural networks that are provably robust to norm-bounded adversarial perturbations. Most of these methods are based on minimizing an upper bound on the worst-case loss over all possible adversarial perturbations. While these techniques show promise, they often result in difficult optimization procedures that remain hard to scale to larger networks. Through a comprehensive analysis, we show how a simple bounding technique, interval bound propagation (IBP), can be exploited to train large provably robust neural networks that beat the state-","authors_text":"Chongli Qin, Jonathan Uesato, Krishnamurthy Dvijotham, Pushmeet Kohli, Relja Arandjelovic, Robert Stanforth, Rudy Bunel, Sven Gowal, Timothy Mann","cross_cats":["cs.CR","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-30T13:12:47Z","title":"On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.12715","kind":"arxiv","version":4},"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:c4a596b209fa50e2446a23c166513b0384c6d604643fa56ab57f2c3b5f72e39a","target":"record","created_at":"2026-07-05T00:00:23Z","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":"face99cec17541bc86d15b3e2960cc6d6f265ecd4771e75918ec2a348fe1c533","cross_cats_sorted":["cs.CR","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-30T13:12:47Z","title_canon_sha256":"6c841c7bfbcb6bc7a03ca755b062a12f6a9d43e7c673a0b4958139e436d21cc8"},"schema_version":"1.0","source":{"id":"1810.12715","kind":"arxiv","version":4}},"canonical_sha256":"d8cc8cda5f2fa995230c9cc174b90b92eacf5d3f79880a79f88480602e703102","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d8cc8cda5f2fa995230c9cc174b90b92eacf5d3f79880a79f88480602e703102","first_computed_at":"2026-07-05T00:00:23.625637Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:00:23.625637Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6cM78Y+dBLfI+K1bYyyH7rJEPzVqb24HGT4f/64+HNMkbK+u1UP0GHhICpdMHefD214/3VKXDFOlo33v4PoDBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T00:00:23.626058Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.12715","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c4a596b209fa50e2446a23c166513b0384c6d604643fa56ab57f2c3b5f72e39a","sha256:828d3516e8067e9810e1f26299247e5b64656d16a62b903f78e85ce73673e9b6"],"state_sha256":"cbad6487d71806df7a5f3d215e2391bf55e951d5dd52a43d6da59ed074d6a5fe"}