{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:UEUE4YCD4HZWKNZCM5I3KJXQYN","short_pith_number":"pith:UEUE4YCD","canonical_record":{"source":{"id":"1908.05474","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-08-15T09:58:24Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"e788f5995e64c338de7d33e165c1b8c764812c302bf213f4a80ebfed1e9afff3","abstract_canon_sha256":"71ae191f10d5697896e26e9596f0d7a563bd40bfee41d58ea5c1cce859afde8e"},"schema_version":"1.0"},"canonical_sha256":"a1284e6043e1f36537226751b526f0c3770ef391717bc2c4f1eb1dae32b3290b","source":{"kind":"arxiv","id":"1908.05474","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.05474","created_at":"2026-07-04T23:56:52Z"},{"alias_kind":"arxiv_version","alias_value":"1908.05474v1","created_at":"2026-07-04T23:56:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.05474","created_at":"2026-07-04T23:56:52Z"},{"alias_kind":"pith_short_12","alias_value":"UEUE4YCD4HZW","created_at":"2026-07-04T23:56:52Z"},{"alias_kind":"pith_short_16","alias_value":"UEUE4YCD4HZWKNZC","created_at":"2026-07-04T23:56:52Z"},{"alias_kind":"pith_short_8","alias_value":"UEUE4YCD","created_at":"2026-07-04T23:56:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:UEUE4YCD4HZWKNZCM5I3KJXQYN","target":"record","payload":{"canonical_record":{"source":{"id":"1908.05474","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-08-15T09:58:24Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"e788f5995e64c338de7d33e165c1b8c764812c302bf213f4a80ebfed1e9afff3","abstract_canon_sha256":"71ae191f10d5697896e26e9596f0d7a563bd40bfee41d58ea5c1cce859afde8e"},"schema_version":"1.0"},"canonical_sha256":"a1284e6043e1f36537226751b526f0c3770ef391717bc2c4f1eb1dae32b3290b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:56:52.667111Z","signature_b64":"kNQ4jZV+VAoLyyJB8+E73YSZWSdIFfZ4JOEJt1kdhpkWfGXaHVDOnoIDfqUJBvLMb63b9g+QGVzS6Vk4LWxjCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a1284e6043e1f36537226751b526f0c3770ef391717bc2c4f1eb1dae32b3290b","last_reissued_at":"2026-07-04T23:56:52.666683Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:56:52.666683Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1908.05474","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-04T23:56:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"M9ochsfiVAVOZqeDMCERunIwAfpRl4MDwHD+kO9htdBo0rol2r2TLYDJVVXmaC00epVLDUYltEa2U96rLfjFDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T00:09:43.978517Z"},"content_sha256":"eaf522ad1ccf5317f2dfa34496f199e2b0afb4dda51a367ccc6a6d7708750512","schema_version":"1.0","event_id":"sha256:eaf522ad1ccf5317f2dfa34496f199e2b0afb4dda51a367ccc6a6d7708750512"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:UEUE4YCD4HZWKNZCM5I3KJXQYN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Adaptive Regularization of Labels","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Hao Sun, Jiadong Guo, Qianggang Ding, Shu-Tao Xia, Sifan Wu","submitted_at":"2019-08-15T09:58:24Z","abstract_excerpt":"Recently, a variety of regularization techniques have been widely applied in deep neural networks, such as dropout, batch normalization, data augmentation, and so on. These methods mainly focus on the regularization of weight parameters to prevent overfitting effectively. In addition, label regularization techniques such as label smoothing and label disturbance have also been proposed with the motivation of adding a stochastic perturbation to labels. In this paper, we propose a novel adaptive label regularization method, which enables the neural network to learn from the erroneous experience a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.05474","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/1908.05474/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":"2026-07-04T23:56:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uf7hVBuHgXpQ8Z3kQYDEl7VE0nnEkHHOwu0ISukLmoVCYe2jFrvP3sP3blLttv4idcMeudWaw0THF7KauZW/CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T00:09:43.979033Z"},"content_sha256":"fd1fe08aefdef0ed3e6c91dcdace4457395a9c21c8f54f78c064bc70a67f6d33","schema_version":"1.0","event_id":"sha256:fd1fe08aefdef0ed3e6c91dcdace4457395a9c21c8f54f78c064bc70a67f6d33"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UEUE4YCD4HZWKNZCM5I3KJXQYN/bundle.json","state_url":"https://pith.science/pith/UEUE4YCD4HZWKNZCM5I3KJXQYN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UEUE4YCD4HZWKNZCM5I3KJXQYN/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":"2026-08-11T00:09:43Z","links":{"resolver":"https://pith.science/pith/UEUE4YCD4HZWKNZCM5I3KJXQYN","bundle":"https://pith.science/pith/UEUE4YCD4HZWKNZCM5I3KJXQYN/bundle.json","state":"https://pith.science/pith/UEUE4YCD4HZWKNZCM5I3KJXQYN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UEUE4YCD4HZWKNZCM5I3KJXQYN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:UEUE4YCD4HZWKNZCM5I3KJXQYN","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":"71ae191f10d5697896e26e9596f0d7a563bd40bfee41d58ea5c1cce859afde8e","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-08-15T09:58:24Z","title_canon_sha256":"e788f5995e64c338de7d33e165c1b8c764812c302bf213f4a80ebfed1e9afff3"},"schema_version":"1.0","source":{"id":"1908.05474","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.05474","created_at":"2026-07-04T23:56:52Z"},{"alias_kind":"arxiv_version","alias_value":"1908.05474v1","created_at":"2026-07-04T23:56:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.05474","created_at":"2026-07-04T23:56:52Z"},{"alias_kind":"pith_short_12","alias_value":"UEUE4YCD4HZW","created_at":"2026-07-04T23:56:52Z"},{"alias_kind":"pith_short_16","alias_value":"UEUE4YCD4HZWKNZC","created_at":"2026-07-04T23:56:52Z"},{"alias_kind":"pith_short_8","alias_value":"UEUE4YCD","created_at":"2026-07-04T23:56:52Z"}],"graph_snapshots":[{"event_id":"sha256:fd1fe08aefdef0ed3e6c91dcdace4457395a9c21c8f54f78c064bc70a67f6d33","target":"graph","created_at":"2026-07-04T23:56:52Z","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/1908.05474/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recently, a variety of regularization techniques have been widely applied in deep neural networks, such as dropout, batch normalization, data augmentation, and so on. These methods mainly focus on the regularization of weight parameters to prevent overfitting effectively. In addition, label regularization techniques such as label smoothing and label disturbance have also been proposed with the motivation of adding a stochastic perturbation to labels. In this paper, we propose a novel adaptive label regularization method, which enables the neural network to learn from the erroneous experience a","authors_text":"Hao Sun, Jiadong Guo, Qianggang Ding, Shu-Tao Xia, Sifan Wu","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-08-15T09:58:24Z","title":"Adaptive Regularization of Labels"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.05474","kind":"arxiv","version":1},"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:eaf522ad1ccf5317f2dfa34496f199e2b0afb4dda51a367ccc6a6d7708750512","target":"record","created_at":"2026-07-04T23:56:52Z","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":"71ae191f10d5697896e26e9596f0d7a563bd40bfee41d58ea5c1cce859afde8e","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-08-15T09:58:24Z","title_canon_sha256":"e788f5995e64c338de7d33e165c1b8c764812c302bf213f4a80ebfed1e9afff3"},"schema_version":"1.0","source":{"id":"1908.05474","kind":"arxiv","version":1}},"canonical_sha256":"a1284e6043e1f36537226751b526f0c3770ef391717bc2c4f1eb1dae32b3290b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a1284e6043e1f36537226751b526f0c3770ef391717bc2c4f1eb1dae32b3290b","first_computed_at":"2026-07-04T23:56:52.666683Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-04T23:56:52.666683Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kNQ4jZV+VAoLyyJB8+E73YSZWSdIFfZ4JOEJt1kdhpkWfGXaHVDOnoIDfqUJBvLMb63b9g+QGVzS6Vk4LWxjCw==","signature_status":"signed_v1","signed_at":"2026-07-04T23:56:52.667111Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.05474","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eaf522ad1ccf5317f2dfa34496f199e2b0afb4dda51a367ccc6a6d7708750512","sha256:fd1fe08aefdef0ed3e6c91dcdace4457395a9c21c8f54f78c064bc70a67f6d33"],"state_sha256":"13de72664ec3d45774b97cf2f503aa26c991401c3baaa80ac96d80fc6b8bea56"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NSOfVGC9485ePKFAu+qzU6ExAy8cCS8rSwWfqhjYvxGnrrR6mlyWPcL7+wFl0ceQjDB97N7gFoePBOTGlU4RCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T00:09:43.982921Z","bundle_sha256":"3ed7b2bceeefd39fbc485dcdb3aa8acbcf703fb2d17805ecacd54ea970a4f600"}}