{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:QTPOFAK25U4ILERZDEPAEOEL7D","short_pith_number":"pith:QTPOFAK2","schema_version":"1.0","canonical_sha256":"84dee2815aed38859239191e02388bf8cce6f17f1b19679b8c08a802a2c8caa5","source":{"kind":"arxiv","id":"1412.8307","version":2},"attestation_state":"computed","paper":{"title":"Fast, simple and accurate handwritten digit classification by training shallow neural network classifiers with the 'extreme learning machine' algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.NE","authors_text":"Andr\\'e van Schaik, Jonathan Tapson, Mark D. McDonnell, Migel D. Tissera, Tony Vladusich","submitted_at":"2014-12-29T11:14:59Z","abstract_excerpt":"Recent advances in training deep (multi-layer) architectures have inspired a renaissance in neural network use. For example, deep convolutional networks are becoming the default option for difficult tasks on large datasets, such as image and speech recognition. However, here we show that error rates below 1% on the MNIST handwritten digit benchmark can be replicated with shallow non-convolutional neural networks. This is achieved by training such networks using the 'Extreme Learning Machine' (ELM) approach, which also enables a very rapid training time (~10 minutes). Adding distortions, as is "},"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":"1412.8307","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2014-12-29T11:14:59Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"2f40fd3f562654695d847d68cc69225ffe6bee6c15db231241170039f8ff7253","abstract_canon_sha256":"4471f4c055d05eba8e378763e1d04da1c3d70454c1bdbb5596f08599de55785a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:20:47.211366Z","signature_b64":"rXocNKGwblK6RUgvoJmpH8855+4Qtzad+snFk9wFjQZH/PFgk1/2nAVYuWdkKMeMUgdEZhsXTQzY7ZgoE3tDDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"84dee2815aed38859239191e02388bf8cce6f17f1b19679b8c08a802a2c8caa5","last_reissued_at":"2026-05-18T01:20:47.210870Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:20:47.210870Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fast, simple and accurate handwritten digit classification by training shallow neural network classifiers with the 'extreme learning machine' algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.NE","authors_text":"Andr\\'e van Schaik, Jonathan Tapson, Mark D. McDonnell, Migel D. Tissera, Tony Vladusich","submitted_at":"2014-12-29T11:14:59Z","abstract_excerpt":"Recent advances in training deep (multi-layer) architectures have inspired a renaissance in neural network use. For example, deep convolutional networks are becoming the default option for difficult tasks on large datasets, such as image and speech recognition. However, here we show that error rates below 1% on the MNIST handwritten digit benchmark can be replicated with shallow non-convolutional neural networks. This is achieved by training such networks using the 'Extreme Learning Machine' (ELM) approach, which also enables a very rapid training time (~10 minutes). Adding distortions, as is "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1412.8307","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":"1412.8307","created_at":"2026-05-18T01:20:47.210944+00:00"},{"alias_kind":"arxiv_version","alias_value":"1412.8307v2","created_at":"2026-05-18T01:20:47.210944+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1412.8307","created_at":"2026-05-18T01:20:47.210944+00:00"},{"alias_kind":"pith_short_12","alias_value":"QTPOFAK25U4I","created_at":"2026-05-18T12:28:46.137349+00:00"},{"alias_kind":"pith_short_16","alias_value":"QTPOFAK25U4ILERZ","created_at":"2026-05-18T12:28:46.137349+00:00"},{"alias_kind":"pith_short_8","alias_value":"QTPOFAK2","created_at":"2026-05-18T12:28:46.137349+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/QTPOFAK25U4ILERZDEPAEOEL7D","json":"https://pith.science/pith/QTPOFAK25U4ILERZDEPAEOEL7D.json","graph_json":"https://pith.science/api/pith-number/QTPOFAK25U4ILERZDEPAEOEL7D/graph.json","events_json":"https://pith.science/api/pith-number/QTPOFAK25U4ILERZDEPAEOEL7D/events.json","paper":"https://pith.science/paper/QTPOFAK2"},"agent_actions":{"view_html":"https://pith.science/pith/QTPOFAK25U4ILERZDEPAEOEL7D","download_json":"https://pith.science/pith/QTPOFAK25U4ILERZDEPAEOEL7D.json","view_paper":"https://pith.science/paper/QTPOFAK2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1412.8307&json=true","fetch_graph":"https://pith.science/api/pith-number/QTPOFAK25U4ILERZDEPAEOEL7D/graph.json","fetch_events":"https://pith.science/api/pith-number/QTPOFAK25U4ILERZDEPAEOEL7D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QTPOFAK25U4ILERZDEPAEOEL7D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QTPOFAK25U4ILERZDEPAEOEL7D/action/storage_attestation","attest_author":"https://pith.science/pith/QTPOFAK25U4ILERZDEPAEOEL7D/action/author_attestation","sign_citation":"https://pith.science/pith/QTPOFAK25U4ILERZDEPAEOEL7D/action/citation_signature","submit_replication":"https://pith.science/pith/QTPOFAK25U4ILERZDEPAEOEL7D/action/replication_record"}},"created_at":"2026-05-18T01:20:47.210944+00:00","updated_at":"2026-05-18T01:20:47.210944+00:00"}