{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:QOEO3Q246UB5WHHEFTWGSSVJVV","short_pith_number":"pith:QOEO3Q24","schema_version":"1.0","canonical_sha256":"8388edc35cf503db1ce42cec694aa9ad788f47daebdaad2b42f2537a27f9c8a6","source":{"kind":"arxiv","id":"2407.01467","version":1},"attestation_state":"computed","paper":{"title":"The Balanced-Pairwise-Affinities Feature Transform","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Daniel Shalam, Simon Korman","submitted_at":"2024-06-25T14:28:05Z","abstract_excerpt":"The Balanced-Pairwise-Affinities (BPA) feature transform is designed to upgrade the features of a set of input items to facilitate downstream matching or grouping related tasks. The transformed set encodes a rich representation of high order relations between the input features. A particular min-cost-max-flow fractional matching problem, whose entropy regularized version can be approximated by an optimal transport (OT) optimization, leads to a transform which is efficient, differentiable, equivariant, parameterless and probabilistically interpretable. While the Sinkhorn OT solver has been adap"},"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":"2407.01467","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-25T14:28:05Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"6104347883ea03da3163ce85ddb76a7e378054e23a2981c332c0f69d07e7547a","abstract_canon_sha256":"b3165aa99b6723efb2989bd2efb25fe67add072b62aeb38e2f7569171c3e99ae"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:38:48.434557Z","signature_b64":"GYJDy9fD8sSNfZXSm9hV5OndQbF2Bq/Ck42NPcJICuctruXF9aMlQdKGyDdD16hFOQ3F6VAaQ7ksvLvBUJ8yCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8388edc35cf503db1ce42cec694aa9ad788f47daebdaad2b42f2537a27f9c8a6","last_reissued_at":"2026-07-05T08:38:48.434153Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:38:48.434153Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Balanced-Pairwise-Affinities Feature Transform","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Daniel Shalam, Simon Korman","submitted_at":"2024-06-25T14:28:05Z","abstract_excerpt":"The Balanced-Pairwise-Affinities (BPA) feature transform is designed to upgrade the features of a set of input items to facilitate downstream matching or grouping related tasks. The transformed set encodes a rich representation of high order relations between the input features. A particular min-cost-max-flow fractional matching problem, whose entropy regularized version can be approximated by an optimal transport (OT) optimization, leads to a transform which is efficient, differentiable, equivariant, parameterless and probabilistically interpretable. While the Sinkhorn OT solver has been adap"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.01467","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/2407.01467/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":"2407.01467","created_at":"2026-07-05T08:38:48.434206+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.01467v1","created_at":"2026-07-05T08:38:48.434206+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.01467","created_at":"2026-07-05T08:38:48.434206+00:00"},{"alias_kind":"pith_short_12","alias_value":"QOEO3Q246UB5","created_at":"2026-07-05T08:38:48.434206+00:00"},{"alias_kind":"pith_short_16","alias_value":"QOEO3Q246UB5WHHE","created_at":"2026-07-05T08:38:48.434206+00:00"},{"alias_kind":"pith_short_8","alias_value":"QOEO3Q24","created_at":"2026-07-05T08:38:48.434206+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.09299","citing_title":"ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation","ref_index":50,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QOEO3Q246UB5WHHEFTWGSSVJVV","json":"https://pith.science/pith/QOEO3Q246UB5WHHEFTWGSSVJVV.json","graph_json":"https://pith.science/api/pith-number/QOEO3Q246UB5WHHEFTWGSSVJVV/graph.json","events_json":"https://pith.science/api/pith-number/QOEO3Q246UB5WHHEFTWGSSVJVV/events.json","paper":"https://pith.science/paper/QOEO3Q24"},"agent_actions":{"view_html":"https://pith.science/pith/QOEO3Q246UB5WHHEFTWGSSVJVV","download_json":"https://pith.science/pith/QOEO3Q246UB5WHHEFTWGSSVJVV.json","view_paper":"https://pith.science/paper/QOEO3Q24","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.01467&json=true","fetch_graph":"https://pith.science/api/pith-number/QOEO3Q246UB5WHHEFTWGSSVJVV/graph.json","fetch_events":"https://pith.science/api/pith-number/QOEO3Q246UB5WHHEFTWGSSVJVV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QOEO3Q246UB5WHHEFTWGSSVJVV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QOEO3Q246UB5WHHEFTWGSSVJVV/action/storage_attestation","attest_author":"https://pith.science/pith/QOEO3Q246UB5WHHEFTWGSSVJVV/action/author_attestation","sign_citation":"https://pith.science/pith/QOEO3Q246UB5WHHEFTWGSSVJVV/action/citation_signature","submit_replication":"https://pith.science/pith/QOEO3Q246UB5WHHEFTWGSSVJVV/action/replication_record"}},"created_at":"2026-07-05T08:38:48.434206+00:00","updated_at":"2026-07-05T08:38:48.434206+00:00"}