{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:V4SCL3ITBPYUVXZB3ULAZOAUTM","short_pith_number":"pith:V4SCL3IT","schema_version":"1.0","canonical_sha256":"af2425ed130bf14adf21dd160cb8149b32bad8ffd2b2a645a90cdefac3ecb38b","source":{"kind":"arxiv","id":"1706.03662","version":2},"attestation_state":"computed","paper":{"title":"Practical Gauss-Newton Optimisation for Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Aleksandar Botev, David Barber, Hippolyt Ritter","submitted_at":"2017-06-12T14:39:48Z","abstract_excerpt":"We present an efficient block-diagonal ap- proximation to the Gauss-Newton matrix for feedforward neural networks. Our result- ing algorithm is competitive against state- of-the-art first order optimisation methods, with sometimes significant improvement in optimisation performance. Unlike first-order methods, for which hyperparameter tuning of the optimisation parameters is often a labo- rious process, our approach can provide good performance even when used with default set- tings. A side result of our work is that for piecewise linear transfer functions, the net- work objective function can"},"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":"1706.03662","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-06-12T14:39:48Z","cross_cats_sorted":[],"title_canon_sha256":"d3da0a8e5a930bfb4791046bbd7f395cfc1d01c1ac29a352c0a6ff104914706b","abstract_canon_sha256":"fdb3daa7220985cfbc1f443b1eb3514ddd4d40409150b423a2b7a33f1ca4d475"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:42:28.368992Z","signature_b64":"FzbSbCgiWxEWkbH4Nij7Q+PyTyVsWdhAxZRSLhcXpfi+NGF97wQ51oDEby2CjwA88nGNLLcXoDqfJnNp5x+bDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"af2425ed130bf14adf21dd160cb8149b32bad8ffd2b2a645a90cdefac3ecb38b","last_reissued_at":"2026-05-18T00:42:28.368525Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:42:28.368525Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Practical Gauss-Newton Optimisation for Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Aleksandar Botev, David Barber, Hippolyt Ritter","submitted_at":"2017-06-12T14:39:48Z","abstract_excerpt":"We present an efficient block-diagonal ap- proximation to the Gauss-Newton matrix for feedforward neural networks. Our result- ing algorithm is competitive against state- of-the-art first order optimisation methods, with sometimes significant improvement in optimisation performance. Unlike first-order methods, for which hyperparameter tuning of the optimisation parameters is often a labo- rious process, our approach can provide good performance even when used with default set- tings. A side result of our work is that for piecewise linear transfer functions, the net- work objective function can"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.03662","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":"1706.03662","created_at":"2026-05-18T00:42:28.368601+00:00"},{"alias_kind":"arxiv_version","alias_value":"1706.03662v2","created_at":"2026-05-18T00:42:28.368601+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.03662","created_at":"2026-05-18T00:42:28.368601+00:00"},{"alias_kind":"pith_short_12","alias_value":"V4SCL3ITBPYU","created_at":"2026-05-18T12:31:49.984773+00:00"},{"alias_kind":"pith_short_16","alias_value":"V4SCL3ITBPYUVXZB","created_at":"2026-05-18T12:31:49.984773+00:00"},{"alias_kind":"pith_short_8","alias_value":"V4SCL3IT","created_at":"2026-05-18T12:31:49.984773+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.04959","citing_title":"Parameter identification for predator-prey system with sparse data","ref_index":2,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/V4SCL3ITBPYUVXZB3ULAZOAUTM","json":"https://pith.science/pith/V4SCL3ITBPYUVXZB3ULAZOAUTM.json","graph_json":"https://pith.science/api/pith-number/V4SCL3ITBPYUVXZB3ULAZOAUTM/graph.json","events_json":"https://pith.science/api/pith-number/V4SCL3ITBPYUVXZB3ULAZOAUTM/events.json","paper":"https://pith.science/paper/V4SCL3IT"},"agent_actions":{"view_html":"https://pith.science/pith/V4SCL3ITBPYUVXZB3ULAZOAUTM","download_json":"https://pith.science/pith/V4SCL3ITBPYUVXZB3ULAZOAUTM.json","view_paper":"https://pith.science/paper/V4SCL3IT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1706.03662&json=true","fetch_graph":"https://pith.science/api/pith-number/V4SCL3ITBPYUVXZB3ULAZOAUTM/graph.json","fetch_events":"https://pith.science/api/pith-number/V4SCL3ITBPYUVXZB3ULAZOAUTM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/V4SCL3ITBPYUVXZB3ULAZOAUTM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/V4SCL3ITBPYUVXZB3ULAZOAUTM/action/storage_attestation","attest_author":"https://pith.science/pith/V4SCL3ITBPYUVXZB3ULAZOAUTM/action/author_attestation","sign_citation":"https://pith.science/pith/V4SCL3ITBPYUVXZB3ULAZOAUTM/action/citation_signature","submit_replication":"https://pith.science/pith/V4SCL3ITBPYUVXZB3ULAZOAUTM/action/replication_record"}},"created_at":"2026-05-18T00:42:28.368601+00:00","updated_at":"2026-05-18T00:42:28.368601+00:00"}