{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:G6H3FFWVSBTU6WINGZPYN3NY4J","short_pith_number":"pith:G6H3FFWV","schema_version":"1.0","canonical_sha256":"378fb296d590674f590d365f86edb8e247cd1b929a422f84b73fc1a57a58776f","source":{"kind":"arxiv","id":"2411.16298","version":3},"attestation_state":"computed","paper":{"title":"Evaluating Rank-N-Contrast: Continuous and Robust Representations for Regression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Alexandre Chidiac, Arkin Worlikar, Valentin Six","submitted_at":"2024-11-25T11:31:53Z","abstract_excerpt":"This document is an evaluation of the original \"Rank-N-Contrast\" (arXiv:2210.01189v2) paper published in 2023. This evaluation is done for academic purposes. Deep regression models often fail to capture the continuous nature of sample orders, creating fragmented representations and suboptimal performance. To address this, we reproduced the Rank-N-Contrast (RNC) framework, which learns continuous representations by contrasting samples by their rankings in the target space. Our study validates RNC's theoretical and empirical benefits, including improved performance and robustness. We extended th"},"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":"2411.16298","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-25T11:31:53Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"97f0fb45838f2fb625098d5f47c044330e496944f06f77fa54fde0d42f3e1bc7","abstract_canon_sha256":"6c93acd466e29f6f6e7752dac43c661cbb0620c88e3fbdd8e2565436599bd709"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:25:05.745844Z","signature_b64":"USkPmsgQyzBWTU2KK5TaZ3oHEtbnuy9FHk2xWhoMKvXgZWLtkceMa2UDh9opnPJmRJ5nMALxb0cSHr1Sq/2+Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"378fb296d590674f590d365f86edb8e247cd1b929a422f84b73fc1a57a58776f","last_reissued_at":"2026-07-05T11:25:05.745286Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:25:05.745286Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evaluating Rank-N-Contrast: Continuous and Robust Representations for Regression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Alexandre Chidiac, Arkin Worlikar, Valentin Six","submitted_at":"2024-11-25T11:31:53Z","abstract_excerpt":"This document is an evaluation of the original \"Rank-N-Contrast\" (arXiv:2210.01189v2) paper published in 2023. This evaluation is done for academic purposes. Deep regression models often fail to capture the continuous nature of sample orders, creating fragmented representations and suboptimal performance. To address this, we reproduced the Rank-N-Contrast (RNC) framework, which learns continuous representations by contrasting samples by their rankings in the target space. Our study validates RNC's theoretical and empirical benefits, including improved performance and robustness. We extended th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.16298","kind":"arxiv","version":3},"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/2411.16298/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":"2411.16298","created_at":"2026-07-05T11:25:05.745357+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.16298v3","created_at":"2026-07-05T11:25:05.745357+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.16298","created_at":"2026-07-05T11:25:05.745357+00:00"},{"alias_kind":"pith_short_12","alias_value":"G6H3FFWVSBTU","created_at":"2026-07-05T11:25:05.745357+00:00"},{"alias_kind":"pith_short_16","alias_value":"G6H3FFWVSBTU6WIN","created_at":"2026-07-05T11:25:05.745357+00:00"},{"alias_kind":"pith_short_8","alias_value":"G6H3FFWV","created_at":"2026-07-05T11:25:05.745357+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/G6H3FFWVSBTU6WINGZPYN3NY4J","json":"https://pith.science/pith/G6H3FFWVSBTU6WINGZPYN3NY4J.json","graph_json":"https://pith.science/api/pith-number/G6H3FFWVSBTU6WINGZPYN3NY4J/graph.json","events_json":"https://pith.science/api/pith-number/G6H3FFWVSBTU6WINGZPYN3NY4J/events.json","paper":"https://pith.science/paper/G6H3FFWV"},"agent_actions":{"view_html":"https://pith.science/pith/G6H3FFWVSBTU6WINGZPYN3NY4J","download_json":"https://pith.science/pith/G6H3FFWVSBTU6WINGZPYN3NY4J.json","view_paper":"https://pith.science/paper/G6H3FFWV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.16298&json=true","fetch_graph":"https://pith.science/api/pith-number/G6H3FFWVSBTU6WINGZPYN3NY4J/graph.json","fetch_events":"https://pith.science/api/pith-number/G6H3FFWVSBTU6WINGZPYN3NY4J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G6H3FFWVSBTU6WINGZPYN3NY4J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G6H3FFWVSBTU6WINGZPYN3NY4J/action/storage_attestation","attest_author":"https://pith.science/pith/G6H3FFWVSBTU6WINGZPYN3NY4J/action/author_attestation","sign_citation":"https://pith.science/pith/G6H3FFWVSBTU6WINGZPYN3NY4J/action/citation_signature","submit_replication":"https://pith.science/pith/G6H3FFWVSBTU6WINGZPYN3NY4J/action/replication_record"}},"created_at":"2026-07-05T11:25:05.745357+00:00","updated_at":"2026-07-05T11:25:05.745357+00:00"}