{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:BARIF7BVSZQLWGAFP77KRJIISM","short_pith_number":"pith:BARIF7BV","schema_version":"1.0","canonical_sha256":"082282fc359660bb18057ffea8a5089320454b7e4582a8ae6409cf04fed57d74","source":{"kind":"arxiv","id":"2406.11173","version":5},"attestation_state":"computed","paper":{"title":"BSRBF-KAN: A combination of B-splines and Radial Basis Functions in Kolmogorov-Arnold Networks","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hoang-Thang Ta","submitted_at":"2024-06-17T03:26:02Z","abstract_excerpt":"In this paper, we introduce BSRBF-KAN, a Kolmogorov Arnold Network (KAN) that combines B-splines and radial basis functions (RBFs) to fit input vectors during data training. We perform experiments with BSRBF-KAN, multi-layer perception (MLP), and other popular KANs, including EfficientKAN, FastKAN, FasterKAN, and GottliebKAN over the MNIST and Fashion-MNIST datasets. BSRBF-KAN shows stability in 5 training runs with a competitive average accuracy of 97.55% on MNIST and 89.33% on Fashion-MNIST and obtains convergence better than other networks. We expect BSRBF-KAN to open many combinations of m"},"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":"2406.11173","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-17T03:26:02Z","cross_cats_sorted":[],"title_canon_sha256":"7e70f570657da19dd0c79ab22f33e854e32600352a3566911353907afa1c95bd","abstract_canon_sha256":"c307d90ab9aede83062023c0e11fc1cbe00a8434e70ab5f2539b0cbca078d787"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:28:32.250243Z","signature_b64":"5X3NzwsH3Abm5Ka7gakCdbaE0tjoEV1UrGhTiQuaThZal9FUXtruxkuOPD/8sA1D3Rren5vhsDmHD5ZhZv6/DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"082282fc359660bb18057ffea8a5089320454b7e4582a8ae6409cf04fed57d74","last_reissued_at":"2026-07-05T09:28:32.249704Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:28:32.249704Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BSRBF-KAN: A combination of B-splines and Radial Basis Functions in Kolmogorov-Arnold Networks","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hoang-Thang Ta","submitted_at":"2024-06-17T03:26:02Z","abstract_excerpt":"In this paper, we introduce BSRBF-KAN, a Kolmogorov Arnold Network (KAN) that combines B-splines and radial basis functions (RBFs) to fit input vectors during data training. We perform experiments with BSRBF-KAN, multi-layer perception (MLP), and other popular KANs, including EfficientKAN, FastKAN, FasterKAN, and GottliebKAN over the MNIST and Fashion-MNIST datasets. BSRBF-KAN shows stability in 5 training runs with a competitive average accuracy of 97.55% on MNIST and 89.33% on Fashion-MNIST and obtains convergence better than other networks. We expect BSRBF-KAN to open many combinations of m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.11173","kind":"arxiv","version":5},"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/2406.11173/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2406.11173","created_at":"2026-07-05T09:28:32.249759+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.11173v5","created_at":"2026-07-05T09:28:32.249759+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.11173","created_at":"2026-07-05T09:28:32.249759+00:00"},{"alias_kind":"pith_short_12","alias_value":"BARIF7BVSZQL","created_at":"2026-07-05T09:28:32.249759+00:00"},{"alias_kind":"pith_short_16","alias_value":"BARIF7BVSZQLWGAF","created_at":"2026-07-05T09:28:32.249759+00:00"},{"alias_kind":"pith_short_8","alias_value":"BARIF7BV","created_at":"2026-07-05T09:28:32.249759+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2410.03801","citing_title":"P1-KAN: an effective Kolmogorov-Arnold network with application to hydraulic valley optimization","ref_index":25,"is_internal_anchor":false},{"citing_arxiv_id":"2510.25781","citing_title":"A Practitioner's Guide to Kolmogorov-Arnold Networks","ref_index":43,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BARIF7BVSZQLWGAFP77KRJIISM","json":"https://pith.science/pith/BARIF7BVSZQLWGAFP77KRJIISM.json","graph_json":"https://pith.science/api/pith-number/BARIF7BVSZQLWGAFP77KRJIISM/graph.json","events_json":"https://pith.science/api/pith-number/BARIF7BVSZQLWGAFP77KRJIISM/events.json","paper":"https://pith.science/paper/BARIF7BV"},"agent_actions":{"view_html":"https://pith.science/pith/BARIF7BVSZQLWGAFP77KRJIISM","download_json":"https://pith.science/pith/BARIF7BVSZQLWGAFP77KRJIISM.json","view_paper":"https://pith.science/paper/BARIF7BV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.11173&json=true","fetch_graph":"https://pith.science/api/pith-number/BARIF7BVSZQLWGAFP77KRJIISM/graph.json","fetch_events":"https://pith.science/api/pith-number/BARIF7BVSZQLWGAFP77KRJIISM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BARIF7BVSZQLWGAFP77KRJIISM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BARIF7BVSZQLWGAFP77KRJIISM/action/storage_attestation","attest_author":"https://pith.science/pith/BARIF7BVSZQLWGAFP77KRJIISM/action/author_attestation","sign_citation":"https://pith.science/pith/BARIF7BVSZQLWGAFP77KRJIISM/action/citation_signature","submit_replication":"https://pith.science/pith/BARIF7BVSZQLWGAFP77KRJIISM/action/replication_record"}},"created_at":"2026-07-05T09:28:32.249759+00:00","updated_at":"2026-07-05T09:28:32.249759+00:00"}