{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:H2KXLSVXST62DRLXO3RAZPX635","short_pith_number":"pith:H2KXLSVX","schema_version":"1.0","canonical_sha256":"3e9575cab794fda1c57776e20cbefedf48f9d1a247fca0c9da502e1a04ef5386","source":{"kind":"arxiv","id":"1510.04189","version":2},"attestation_state":"computed","paper":{"title":"Improving Back-Propagation by Adding an Adversarial Gradient","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Arild N{\\o}kland","submitted_at":"2015-10-14T16:27:28Z","abstract_excerpt":"The back-propagation algorithm is widely used for learning in artificial neural networks. A challenge in machine learning is to create models that generalize to new data samples not seen in the training data. Recently, a common flaw in several machine learning algorithms was discovered: small perturbations added to the input data lead to consistent misclassification of data samples. Samples that easily mislead the model are called adversarial examples. Training a \"maxout\" network on adversarial examples has shown to decrease this vulnerability, but also increase classification performance. Thi"},"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":"1510.04189","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-10-14T16:27:28Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"35781a55f64132d0ce9110dde337364b12336b4db35022226fcccfd987058a94","abstract_canon_sha256":"5921287506aceab256fb76b7d044e67cd960dc07c87f545073da436b34489c36"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:17:37.154883Z","signature_b64":"XGfsyaZtz+cWIhP99W2+vNITcjagHECdOhBTHdfdy/EIyi8kr/WOUsdACxlUZjWB7gy5aSljsvLRB782dJ1rBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3e9575cab794fda1c57776e20cbefedf48f9d1a247fca0c9da502e1a04ef5386","last_reissued_at":"2026-05-18T01:17:37.154370Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:17:37.154370Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving Back-Propagation by Adding an Adversarial Gradient","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Arild N{\\o}kland","submitted_at":"2015-10-14T16:27:28Z","abstract_excerpt":"The back-propagation algorithm is widely used for learning in artificial neural networks. A challenge in machine learning is to create models that generalize to new data samples not seen in the training data. Recently, a common flaw in several machine learning algorithms was discovered: small perturbations added to the input data lead to consistent misclassification of data samples. Samples that easily mislead the model are called adversarial examples. Training a \"maxout\" network on adversarial examples has shown to decrease this vulnerability, but also increase classification performance. Thi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1510.04189","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1510.04189","created_at":"2026-05-18T01:17:37.154445+00:00"},{"alias_kind":"arxiv_version","alias_value":"1510.04189v2","created_at":"2026-05-18T01:17:37.154445+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1510.04189","created_at":"2026-05-18T01:17:37.154445+00:00"},{"alias_kind":"pith_short_12","alias_value":"H2KXLSVXST62","created_at":"2026-05-18T12:29:22.688609+00:00"},{"alias_kind":"pith_short_16","alias_value":"H2KXLSVXST62DRLX","created_at":"2026-05-18T12:29:22.688609+00:00"},{"alias_kind":"pith_short_8","alias_value":"H2KXLSVX","created_at":"2026-05-18T12:29:22.688609+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/H2KXLSVXST62DRLXO3RAZPX635","json":"https://pith.science/pith/H2KXLSVXST62DRLXO3RAZPX635.json","graph_json":"https://pith.science/api/pith-number/H2KXLSVXST62DRLXO3RAZPX635/graph.json","events_json":"https://pith.science/api/pith-number/H2KXLSVXST62DRLXO3RAZPX635/events.json","paper":"https://pith.science/paper/H2KXLSVX"},"agent_actions":{"view_html":"https://pith.science/pith/H2KXLSVXST62DRLXO3RAZPX635","download_json":"https://pith.science/pith/H2KXLSVXST62DRLXO3RAZPX635.json","view_paper":"https://pith.science/paper/H2KXLSVX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1510.04189&json=true","fetch_graph":"https://pith.science/api/pith-number/H2KXLSVXST62DRLXO3RAZPX635/graph.json","fetch_events":"https://pith.science/api/pith-number/H2KXLSVXST62DRLXO3RAZPX635/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H2KXLSVXST62DRLXO3RAZPX635/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H2KXLSVXST62DRLXO3RAZPX635/action/storage_attestation","attest_author":"https://pith.science/pith/H2KXLSVXST62DRLXO3RAZPX635/action/author_attestation","sign_citation":"https://pith.science/pith/H2KXLSVXST62DRLXO3RAZPX635/action/citation_signature","submit_replication":"https://pith.science/pith/H2KXLSVXST62DRLXO3RAZPX635/action/replication_record"}},"created_at":"2026-05-18T01:17:37.154445+00:00","updated_at":"2026-05-18T01:17:37.154445+00:00"}