{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:MHILTTRJUYL7D3JYM32EFJCJW2","short_pith_number":"pith:MHILTTRJ","canonical_record":{"source":{"id":"1905.10626","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-25T16:11:14Z","cross_cats_sorted":["cs.CR","stat.ML"],"title_canon_sha256":"db1bc711ba166b2a2377cb753cd97eacf40ff7c6fb251eeb2344fd6bb77418f2","abstract_canon_sha256":"73dafeb1b6166fc8ab29786b57f98d1e6fe97b613b6ddf07e4abf0eff8739311"},"schema_version":"1.0"},"canonical_sha256":"61d0b9ce29a617f1ed3866f442a449b68e3fa18a0727432e1e0222da77b87c8c","source":{"kind":"arxiv","id":"1905.10626","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.10626","created_at":"2026-07-05T00:42:31Z"},{"alias_kind":"arxiv_version","alias_value":"1905.10626v3","created_at":"2026-07-05T00:42:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.10626","created_at":"2026-07-05T00:42:31Z"},{"alias_kind":"pith_short_12","alias_value":"MHILTTRJUYL7","created_at":"2026-07-05T00:42:31Z"},{"alias_kind":"pith_short_16","alias_value":"MHILTTRJUYL7D3JY","created_at":"2026-07-05T00:42:31Z"},{"alias_kind":"pith_short_8","alias_value":"MHILTTRJ","created_at":"2026-07-05T00:42:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:MHILTTRJUYL7D3JYM32EFJCJW2","target":"record","payload":{"canonical_record":{"source":{"id":"1905.10626","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-25T16:11:14Z","cross_cats_sorted":["cs.CR","stat.ML"],"title_canon_sha256":"db1bc711ba166b2a2377cb753cd97eacf40ff7c6fb251eeb2344fd6bb77418f2","abstract_canon_sha256":"73dafeb1b6166fc8ab29786b57f98d1e6fe97b613b6ddf07e4abf0eff8739311"},"schema_version":"1.0"},"canonical_sha256":"61d0b9ce29a617f1ed3866f442a449b68e3fa18a0727432e1e0222da77b87c8c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:42:31.169027Z","signature_b64":"xJgXXFgqNOA7RgX+lwlgIBDUrP/4id+vocXHY3oOk9SlM3Ct4RF66JeYMTNWwUa1Cn/8Y40+Jv0E/6RXCaOOAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"61d0b9ce29a617f1ed3866f442a449b68e3fa18a0727432e1e0222da77b87c8c","last_reissued_at":"2026-07-05T00:42:31.168531Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:42:31.168531Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.10626","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:42:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HU0h+xuS22Z60w3c+xiC5UsMtqfHWOlOEbOqJ50MBirnk+e15NR5Euk4GvXt6Qh35BMsfwpeQ+oddnASxM4vBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T19:34:44.088551Z"},"content_sha256":"08706402e49bae9ad8de46e9a6e1d24312e1d7d8552c26c5b2d04edf56446028","schema_version":"1.0","event_id":"sha256:08706402e49bae9ad8de46e9a6e1d24312e1d7d8552c26c5b2d04edf56446028"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:MHILTTRJUYL7D3JYM32EFJCJW2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","stat.ML"],"primary_cat":"cs.LG","authors_text":"Chao Du, Jun Zhu, Kun Xu, Ning Chen, Tianyu Pang, Yinpeng Dong","submitted_at":"2019-05-25T16:11:14Z","abstract_excerpt":"Previous work shows that adversarially robust generalization requires larger sample complexity, and the same dataset, e.g., CIFAR-10, which enables good standard accuracy may not suffice to train robust models. Since collecting new training data could be costly, we focus on better utilizing the given data by inducing the regions with high sample density in the feature space, which could lead to locally sufficient samples for robust learning. We first formally show that the softmax cross-entropy (SCE) loss and its variants convey inappropriate supervisory signals, which encourage the learned fe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.10626","kind":"arxiv","version":3},"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/1905.10626/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":"2026-07-05T00:42:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pCzoo9MQ0NMbeyFtwsN+1MfedunmcEY8Go4pGjWWz/E6eYe8D5x6AtB/VdIOLzu+RYJnOqtHZG6+jZ+8RrN1Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T19:34:44.089076Z"},"content_sha256":"cbd71538c2a2ab058db3cd5ab183772497f9ae9a44f3339cda2e658754d869d0","schema_version":"1.0","event_id":"sha256:cbd71538c2a2ab058db3cd5ab183772497f9ae9a44f3339cda2e658754d869d0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MHILTTRJUYL7D3JYM32EFJCJW2/bundle.json","state_url":"https://pith.science/pith/MHILTTRJUYL7D3JYM32EFJCJW2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MHILTTRJUYL7D3JYM32EFJCJW2/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":"2026-08-11T19:34:44Z","links":{"resolver":"https://pith.science/pith/MHILTTRJUYL7D3JYM32EFJCJW2","bundle":"https://pith.science/pith/MHILTTRJUYL7D3JYM32EFJCJW2/bundle.json","state":"https://pith.science/pith/MHILTTRJUYL7D3JYM32EFJCJW2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MHILTTRJUYL7D3JYM32EFJCJW2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:MHILTTRJUYL7D3JYM32EFJCJW2","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":"73dafeb1b6166fc8ab29786b57f98d1e6fe97b613b6ddf07e4abf0eff8739311","cross_cats_sorted":["cs.CR","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-25T16:11:14Z","title_canon_sha256":"db1bc711ba166b2a2377cb753cd97eacf40ff7c6fb251eeb2344fd6bb77418f2"},"schema_version":"1.0","source":{"id":"1905.10626","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.10626","created_at":"2026-07-05T00:42:31Z"},{"alias_kind":"arxiv_version","alias_value":"1905.10626v3","created_at":"2026-07-05T00:42:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.10626","created_at":"2026-07-05T00:42:31Z"},{"alias_kind":"pith_short_12","alias_value":"MHILTTRJUYL7","created_at":"2026-07-05T00:42:31Z"},{"alias_kind":"pith_short_16","alias_value":"MHILTTRJUYL7D3JY","created_at":"2026-07-05T00:42:31Z"},{"alias_kind":"pith_short_8","alias_value":"MHILTTRJ","created_at":"2026-07-05T00:42:31Z"}],"graph_snapshots":[{"event_id":"sha256:cbd71538c2a2ab058db3cd5ab183772497f9ae9a44f3339cda2e658754d869d0","target":"graph","created_at":"2026-07-05T00:42:31Z","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/1905.10626/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Previous work shows that adversarially robust generalization requires larger sample complexity, and the same dataset, e.g., CIFAR-10, which enables good standard accuracy may not suffice to train robust models. Since collecting new training data could be costly, we focus on better utilizing the given data by inducing the regions with high sample density in the feature space, which could lead to locally sufficient samples for robust learning. We first formally show that the softmax cross-entropy (SCE) loss and its variants convey inappropriate supervisory signals, which encourage the learned fe","authors_text":"Chao Du, Jun Zhu, Kun Xu, Ning Chen, Tianyu Pang, Yinpeng Dong","cross_cats":["cs.CR","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-25T16:11:14Z","title":"Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.10626","kind":"arxiv","version":3},"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":"sha256:08706402e49bae9ad8de46e9a6e1d24312e1d7d8552c26c5b2d04edf56446028","target":"record","created_at":"2026-07-05T00:42:31Z","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":"73dafeb1b6166fc8ab29786b57f98d1e6fe97b613b6ddf07e4abf0eff8739311","cross_cats_sorted":["cs.CR","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-25T16:11:14Z","title_canon_sha256":"db1bc711ba166b2a2377cb753cd97eacf40ff7c6fb251eeb2344fd6bb77418f2"},"schema_version":"1.0","source":{"id":"1905.10626","kind":"arxiv","version":3}},"canonical_sha256":"61d0b9ce29a617f1ed3866f442a449b68e3fa18a0727432e1e0222da77b87c8c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"61d0b9ce29a617f1ed3866f442a449b68e3fa18a0727432e1e0222da77b87c8c","first_computed_at":"2026-07-05T00:42:31.168531Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:42:31.168531Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xJgXXFgqNOA7RgX+lwlgIBDUrP/4id+vocXHY3oOk9SlM3Ct4RF66JeYMTNWwUa1Cn/8Y40+Jv0E/6RXCaOOAg==","signature_status":"signed_v1","signed_at":"2026-07-05T00:42:31.169027Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.10626","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:08706402e49bae9ad8de46e9a6e1d24312e1d7d8552c26c5b2d04edf56446028","sha256:cbd71538c2a2ab058db3cd5ab183772497f9ae9a44f3339cda2e658754d869d0"],"state_sha256":"4bcb06237c273c6a36304b55ebe75132e0754b8af174fdddaffbe086c9a02843"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RQ3WRNs45oq2VZh8tzANYNh/VhGWFDxv45PXHD1GcSdlHqmMO3VMn8qWbJYcYlbY2DinkyHiKnWmCrKfowJdAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T19:34:44.093123Z","bundle_sha256":"85af7eaf7bcca4975c150a7a4b3cdff975a57573603d0888181bfe62d9925cfd"}}