{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:AH4SURWAXRIQFLXFDRZ67LOUDY","short_pith_number":"pith:AH4SURWA","canonical_record":{"source":{"id":"1905.00877","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-05-02T17:46:06Z","cross_cats_sorted":["cs.LG","math.OC"],"title_canon_sha256":"faa84f93415d05d275ee86af9ff3dee6e5ccb45d2010d8a13dc5f503ff861e0d","abstract_canon_sha256":"a733e4fb07092d4798d503cc345ecc96a0e48a1414c281c122486a17bae3c854"},"schema_version":"1.0"},"canonical_sha256":"01f92a46c0bc5102aee51c73efadd41e2b99469adf96ba08ad6b97a2a2e0617d","source":{"kind":"arxiv","id":"1905.00877","version":6},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.00877","created_at":"2026-07-05T00:16:19Z"},{"alias_kind":"arxiv_version","alias_value":"1905.00877v6","created_at":"2026-07-05T00:16:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.00877","created_at":"2026-07-05T00:16:19Z"},{"alias_kind":"pith_short_12","alias_value":"AH4SURWAXRIQ","created_at":"2026-07-05T00:16:19Z"},{"alias_kind":"pith_short_16","alias_value":"AH4SURWAXRIQFLXF","created_at":"2026-07-05T00:16:19Z"},{"alias_kind":"pith_short_8","alias_value":"AH4SURWA","created_at":"2026-07-05T00:16:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:AH4SURWAXRIQFLXFDRZ67LOUDY","target":"record","payload":{"canonical_record":{"source":{"id":"1905.00877","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-05-02T17:46:06Z","cross_cats_sorted":["cs.LG","math.OC"],"title_canon_sha256":"faa84f93415d05d275ee86af9ff3dee6e5ccb45d2010d8a13dc5f503ff861e0d","abstract_canon_sha256":"a733e4fb07092d4798d503cc345ecc96a0e48a1414c281c122486a17bae3c854"},"schema_version":"1.0"},"canonical_sha256":"01f92a46c0bc5102aee51c73efadd41e2b99469adf96ba08ad6b97a2a2e0617d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:16:19.992985Z","signature_b64":"VbY44pQ/kMwpC31ERdK99GsbWQ4ddLJv+r7nzwoq8EJ4oYZrzKFucD/BMeF8x70NeRDRUeB3oi1Xn9wJVNBaCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"01f92a46c0bc5102aee51c73efadd41e2b99469adf96ba08ad6b97a2a2e0617d","last_reissued_at":"2026-07-05T00:16:19.992484Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:16:19.992484Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.00877","source_version":6,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:16:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BmespjISWXLkbP3SQdJoWEanV4yEe/28FIrdsf0l1jD+7BPwNv42bZI9gZQxHRvIqIrbESNN2UOnGnV6qQR+Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T15:37:37.331398Z"},"content_sha256":"139590ff83271985653ead1f12015a69f87bd783dc45443f98bafd51edc3c01f","schema_version":"1.0","event_id":"sha256:139590ff83271985653ead1f12015a69f87bd783dc45443f98bafd51edc3c01f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:AH4SURWAXRIQFLXFDRZ67LOUDY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.OC"],"primary_cat":"stat.ML","authors_text":"Bin Dong, Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu","submitted_at":"2019-05-02T17:46:06Z","abstract_excerpt":"Deep learning achieves state-of-the-art results in many tasks in computer vision and natural language processing. However, recent works have shown that deep networks can be vulnerable to adversarial perturbations, which raised a serious robustness issue of deep networks. Adversarial training, typically formulated as a robust optimization problem, is an effective way of improving the robustness of deep networks. A major drawback of existing adversarial training algorithms is the computational overhead of the generation of adversarial examples, typically far greater than that of the network trai"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.00877","kind":"arxiv","version":6},"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/1905.00877/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:16:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tYxrEgCU8YBoy79G4oAQM8vFA6KgHcgwTHoKmxaUX0L2oHIM0fqtqDDW//05C2OQBMMjv5OrxrdLjlh7DM+ICg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T15:37:37.331781Z"},"content_sha256":"7c56e07cd42fae0771ba5669f2cf9481e3c81654e710c688ddee683a622548a0","schema_version":"1.0","event_id":"sha256:7c56e07cd42fae0771ba5669f2cf9481e3c81654e710c688ddee683a622548a0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AH4SURWAXRIQFLXFDRZ67LOUDY/bundle.json","state_url":"https://pith.science/pith/AH4SURWAXRIQFLXFDRZ67LOUDY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AH4SURWAXRIQFLXFDRZ67LOUDY/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-07T15:37:37Z","links":{"resolver":"https://pith.science/pith/AH4SURWAXRIQFLXFDRZ67LOUDY","bundle":"https://pith.science/pith/AH4SURWAXRIQFLXFDRZ67LOUDY/bundle.json","state":"https://pith.science/pith/AH4SURWAXRIQFLXFDRZ67LOUDY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AH4SURWAXRIQFLXFDRZ67LOUDY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:AH4SURWAXRIQFLXFDRZ67LOUDY","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":"a733e4fb07092d4798d503cc345ecc96a0e48a1414c281c122486a17bae3c854","cross_cats_sorted":["cs.LG","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-05-02T17:46:06Z","title_canon_sha256":"faa84f93415d05d275ee86af9ff3dee6e5ccb45d2010d8a13dc5f503ff861e0d"},"schema_version":"1.0","source":{"id":"1905.00877","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.00877","created_at":"2026-07-05T00:16:19Z"},{"alias_kind":"arxiv_version","alias_value":"1905.00877v6","created_at":"2026-07-05T00:16:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.00877","created_at":"2026-07-05T00:16:19Z"},{"alias_kind":"pith_short_12","alias_value":"AH4SURWAXRIQ","created_at":"2026-07-05T00:16:19Z"},{"alias_kind":"pith_short_16","alias_value":"AH4SURWAXRIQFLXF","created_at":"2026-07-05T00:16:19Z"},{"alias_kind":"pith_short_8","alias_value":"AH4SURWA","created_at":"2026-07-05T00:16:19Z"}],"graph_snapshots":[{"event_id":"sha256:7c56e07cd42fae0771ba5669f2cf9481e3c81654e710c688ddee683a622548a0","target":"graph","created_at":"2026-07-05T00:16:19Z","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/1905.00877/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep learning achieves state-of-the-art results in many tasks in computer vision and natural language processing. However, recent works have shown that deep networks can be vulnerable to adversarial perturbations, which raised a serious robustness issue of deep networks. Adversarial training, typically formulated as a robust optimization problem, is an effective way of improving the robustness of deep networks. A major drawback of existing adversarial training algorithms is the computational overhead of the generation of adversarial examples, typically far greater than that of the network trai","authors_text":"Bin Dong, Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu","cross_cats":["cs.LG","math.OC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-05-02T17:46:06Z","title":"You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.00877","kind":"arxiv","version":6},"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":"sha256:139590ff83271985653ead1f12015a69f87bd783dc45443f98bafd51edc3c01f","target":"record","created_at":"2026-07-05T00:16:19Z","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":"a733e4fb07092d4798d503cc345ecc96a0e48a1414c281c122486a17bae3c854","cross_cats_sorted":["cs.LG","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-05-02T17:46:06Z","title_canon_sha256":"faa84f93415d05d275ee86af9ff3dee6e5ccb45d2010d8a13dc5f503ff861e0d"},"schema_version":"1.0","source":{"id":"1905.00877","kind":"arxiv","version":6}},"canonical_sha256":"01f92a46c0bc5102aee51c73efadd41e2b99469adf96ba08ad6b97a2a2e0617d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"01f92a46c0bc5102aee51c73efadd41e2b99469adf96ba08ad6b97a2a2e0617d","first_computed_at":"2026-07-05T00:16:19.992484Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:16:19.992484Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VbY44pQ/kMwpC31ERdK99GsbWQ4ddLJv+r7nzwoq8EJ4oYZrzKFucD/BMeF8x70NeRDRUeB3oi1Xn9wJVNBaCw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:16:19.992985Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.00877","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:139590ff83271985653ead1f12015a69f87bd783dc45443f98bafd51edc3c01f","sha256:7c56e07cd42fae0771ba5669f2cf9481e3c81654e710c688ddee683a622548a0"],"state_sha256":"c8d48e291fb3332d0a05fa54b54ffbb4b1a7e8df3a3810f21fd777799223818a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xGoVLs+2mhWOvQar5h4UsoSnywM1n8ISIakqZGD/2RXl75XEN/QG5Lf90nDGNGiDsTv5TorCk7P8ADvhPmDhBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T15:37:37.333715Z","bundle_sha256":"228d2735fa2e8fd9c504bac3fe1ef555224b9388a59ed4b2aaa54fbf851cc4be"}}