{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:UVNT7HF2SVKUXAW654Z4GUTEAQ","short_pith_number":"pith:UVNT7HF2","schema_version":"1.0","canonical_sha256":"a55b3f9cba95554b82deef33c352640422c420a5a34f5cfae19dd844d07d3708","source":{"kind":"arxiv","id":"2202.06866","version":1},"attestation_state":"computed","paper":{"title":"Delaunay Component Analysis for Evaluation of Data Representations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Anastasia Varava, Danica Kragic, Florian Pokorny, Petra Poklukar, Vladislav Polianskii","submitted_at":"2022-02-14T16:48:23Z","abstract_excerpt":"Advanced representation learning techniques require reliable and general evaluation methods. Recently, several algorithms based on the common idea of geometric and topological analysis of a manifold approximated from the learned data representations have been proposed. In this work, we introduce Delaunay Component Analysis (DCA) - an evaluation algorithm which approximates the data manifold using a more suitable neighbourhood graph called Delaunay graph. This provides a reliable manifold estimation even for challenging geometric arrangements of representations such as clusters with varying sha"},"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":"2202.06866","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-02-14T16:48:23Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"73ee915ebd3aa3dbdbe38dbde84bc25284e3d77e41070cc91866454bdc7e81f8","abstract_canon_sha256":"122c6559a415bc202a9a53c2a4a1ba186e57c750123c074abe46fc2cebec08c5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:56:42.317062Z","signature_b64":"fAS7BkuUXJdeP9+fVbxukTCShp4ypOHV3dzCWgTHO6HMzhH/VZ6s05KtBFaOYO751F1DczHCwFqSgz9dykWRCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a55b3f9cba95554b82deef33c352640422c420a5a34f5cfae19dd844d07d3708","last_reissued_at":"2026-07-05T03:56:42.316667Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:56:42.316667Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Delaunay Component Analysis for Evaluation of Data Representations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Anastasia Varava, Danica Kragic, Florian Pokorny, Petra Poklukar, Vladislav Polianskii","submitted_at":"2022-02-14T16:48:23Z","abstract_excerpt":"Advanced representation learning techniques require reliable and general evaluation methods. Recently, several algorithms based on the common idea of geometric and topological analysis of a manifold approximated from the learned data representations have been proposed. In this work, we introduce Delaunay Component Analysis (DCA) - an evaluation algorithm which approximates the data manifold using a more suitable neighbourhood graph called Delaunay graph. This provides a reliable manifold estimation even for challenging geometric arrangements of representations such as clusters with varying sha"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.06866","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/2202.06866/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":"2202.06866","created_at":"2026-07-05T03:56:42.316730+00:00"},{"alias_kind":"arxiv_version","alias_value":"2202.06866v1","created_at":"2026-07-05T03:56:42.316730+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.06866","created_at":"2026-07-05T03:56:42.316730+00:00"},{"alias_kind":"pith_short_12","alias_value":"UVNT7HF2SVKU","created_at":"2026-07-05T03:56:42.316730+00:00"},{"alias_kind":"pith_short_16","alias_value":"UVNT7HF2SVKUXAW6","created_at":"2026-07-05T03:56:42.316730+00:00"},{"alias_kind":"pith_short_8","alias_value":"UVNT7HF2","created_at":"2026-07-05T03:56:42.316730+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/UVNT7HF2SVKUXAW654Z4GUTEAQ","json":"https://pith.science/pith/UVNT7HF2SVKUXAW654Z4GUTEAQ.json","graph_json":"https://pith.science/api/pith-number/UVNT7HF2SVKUXAW654Z4GUTEAQ/graph.json","events_json":"https://pith.science/api/pith-number/UVNT7HF2SVKUXAW654Z4GUTEAQ/events.json","paper":"https://pith.science/paper/UVNT7HF2"},"agent_actions":{"view_html":"https://pith.science/pith/UVNT7HF2SVKUXAW654Z4GUTEAQ","download_json":"https://pith.science/pith/UVNT7HF2SVKUXAW654Z4GUTEAQ.json","view_paper":"https://pith.science/paper/UVNT7HF2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2202.06866&json=true","fetch_graph":"https://pith.science/api/pith-number/UVNT7HF2SVKUXAW654Z4GUTEAQ/graph.json","fetch_events":"https://pith.science/api/pith-number/UVNT7HF2SVKUXAW654Z4GUTEAQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UVNT7HF2SVKUXAW654Z4GUTEAQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UVNT7HF2SVKUXAW654Z4GUTEAQ/action/storage_attestation","attest_author":"https://pith.science/pith/UVNT7HF2SVKUXAW654Z4GUTEAQ/action/author_attestation","sign_citation":"https://pith.science/pith/UVNT7HF2SVKUXAW654Z4GUTEAQ/action/citation_signature","submit_replication":"https://pith.science/pith/UVNT7HF2SVKUXAW654Z4GUTEAQ/action/replication_record"}},"created_at":"2026-07-05T03:56:42.316730+00:00","updated_at":"2026-07-05T03:56:42.316730+00:00"}