{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:7ADWIBOTFKTATQIZEHH6KAL3XF","short_pith_number":"pith:7ADWIBOT","canonical_record":{"source":{"id":"2407.03257","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-07-03T16:38:57Z","cross_cats_sorted":[],"title_canon_sha256":"71d8f141e62eb7cacf228e4c2b85dbeee161c1090cc38ca6a7f1d22c92da788d","abstract_canon_sha256":"e17f94cae2e872535820d388fc39916574d4f1eb548488ca6e9958d95ace0f7c"},"schema_version":"1.0"},"canonical_sha256":"f8076405d32aa609c11921cfe5017bb95635272616c2700dcb8abe5ad1385634","source":{"kind":"arxiv","id":"2407.03257","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.03257","created_at":"2026-07-05T10:22:47Z"},{"alias_kind":"arxiv_version","alias_value":"2407.03257v2","created_at":"2026-07-05T10:22:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.03257","created_at":"2026-07-05T10:22:47Z"},{"alias_kind":"pith_short_12","alias_value":"7ADWIBOTFKTA","created_at":"2026-07-05T10:22:47Z"},{"alias_kind":"pith_short_16","alias_value":"7ADWIBOTFKTATQIZ","created_at":"2026-07-05T10:22:47Z"},{"alias_kind":"pith_short_8","alias_value":"7ADWIBOT","created_at":"2026-07-05T10:22:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:7ADWIBOTFKTATQIZEHH6KAL3XF","target":"record","payload":{"canonical_record":{"source":{"id":"2407.03257","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-07-03T16:38:57Z","cross_cats_sorted":[],"title_canon_sha256":"71d8f141e62eb7cacf228e4c2b85dbeee161c1090cc38ca6a7f1d22c92da788d","abstract_canon_sha256":"e17f94cae2e872535820d388fc39916574d4f1eb548488ca6e9958d95ace0f7c"},"schema_version":"1.0"},"canonical_sha256":"f8076405d32aa609c11921cfe5017bb95635272616c2700dcb8abe5ad1385634","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:22:47.875069Z","signature_b64":"N0VqnAk+A6qIYCnqWpxcFvNEyxIeohqsAXGXjAx0pg5KSOkH6obAir4Pl5WfNoSrGGgHmjj+EarcDBp/FgqEDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f8076405d32aa609c11921cfe5017bb95635272616c2700dcb8abe5ad1385634","last_reissued_at":"2026-07-05T10:22:47.874133Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:22:47.874133Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2407.03257","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:22:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mXw1NEGaDkCR1rjFhETxD6vrYYhColMOEwUWV1sZqFBwi20Sl1T1vqV4NvucvfgNIZSyKuU6lGiwAJNa6Y8WDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T05:08:41.772663Z"},"content_sha256":"384b83ea6b00ac5e1a13293b3bcb8141e8e00a8ea3909ac187b4609fb66eb88e","schema_version":"1.0","event_id":"sha256:384b83ea6b00ac5e1a13293b3bcb8141e8e00a8ea3909ac187b4609fb66eb88e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:7ADWIBOTFKTATQIZEHH6KAL3XF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Revisiting Nearest Neighbor for Tabular Data: A Deep Tabular Baseline Two Decades Later","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"De-Chuan Zhan, Han-Jia Ye, Huai-Hong Yin, Wei-Lun Chao","submitted_at":"2024-07-03T16:38:57Z","abstract_excerpt":"The widespread enthusiasm for deep learning has recently expanded into the domain of tabular data. Recognizing that the advancement in deep tabular methods is often inspired by classical methods, e.g., integration of nearest neighbors into neural networks, we investigate whether these classical methods can be revitalized with modern techniques. We revisit a differentiable version of $K$-nearest neighbors (KNN) -- Neighbourhood Components Analysis (NCA) -- originally designed to learn a linear projection to capture semantic similarities between instances, and seek to gradually add modern deep l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.03257","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2407.03257/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:22:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NdywD5hmhSkAZrb87Mphxklm3v6gX25e4IpmliCslCKBIN+1xdlAOzx1OnzmQXy77uFOWHv8hsCgRf4T3mEVBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T05:08:41.773158Z"},"content_sha256":"bb0b9b3fa7b618c569b09546fb46d323731f983c3cc817873eabebf591f088bf","schema_version":"1.0","event_id":"sha256:bb0b9b3fa7b618c569b09546fb46d323731f983c3cc817873eabebf591f088bf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7ADWIBOTFKTATQIZEHH6KAL3XF/bundle.json","state_url":"https://pith.science/pith/7ADWIBOTFKTATQIZEHH6KAL3XF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7ADWIBOTFKTATQIZEHH6KAL3XF/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-08T05:08:41Z","links":{"resolver":"https://pith.science/pith/7ADWIBOTFKTATQIZEHH6KAL3XF","bundle":"https://pith.science/pith/7ADWIBOTFKTATQIZEHH6KAL3XF/bundle.json","state":"https://pith.science/pith/7ADWIBOTFKTATQIZEHH6KAL3XF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7ADWIBOTFKTATQIZEHH6KAL3XF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:7ADWIBOTFKTATQIZEHH6KAL3XF","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e17f94cae2e872535820d388fc39916574d4f1eb548488ca6e9958d95ace0f7c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-07-03T16:38:57Z","title_canon_sha256":"71d8f141e62eb7cacf228e4c2b85dbeee161c1090cc38ca6a7f1d22c92da788d"},"schema_version":"1.0","source":{"id":"2407.03257","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.03257","created_at":"2026-07-05T10:22:47Z"},{"alias_kind":"arxiv_version","alias_value":"2407.03257v2","created_at":"2026-07-05T10:22:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.03257","created_at":"2026-07-05T10:22:47Z"},{"alias_kind":"pith_short_12","alias_value":"7ADWIBOTFKTA","created_at":"2026-07-05T10:22:47Z"},{"alias_kind":"pith_short_16","alias_value":"7ADWIBOTFKTATQIZ","created_at":"2026-07-05T10:22:47Z"},{"alias_kind":"pith_short_8","alias_value":"7ADWIBOT","created_at":"2026-07-05T10:22:47Z"}],"graph_snapshots":[{"event_id":"sha256:bb0b9b3fa7b618c569b09546fb46d323731f983c3cc817873eabebf591f088bf","target":"graph","created_at":"2026-07-05T10:22:47Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2407.03257/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The widespread enthusiasm for deep learning has recently expanded into the domain of tabular data. Recognizing that the advancement in deep tabular methods is often inspired by classical methods, e.g., integration of nearest neighbors into neural networks, we investigate whether these classical methods can be revitalized with modern techniques. We revisit a differentiable version of $K$-nearest neighbors (KNN) -- Neighbourhood Components Analysis (NCA) -- originally designed to learn a linear projection to capture semantic similarities between instances, and seek to gradually add modern deep l","authors_text":"De-Chuan Zhan, Han-Jia Ye, Huai-Hong Yin, Wei-Lun Chao","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-07-03T16:38:57Z","title":"Revisiting Nearest Neighbor for Tabular Data: A Deep Tabular Baseline Two Decades Later"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.03257","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:384b83ea6b00ac5e1a13293b3bcb8141e8e00a8ea3909ac187b4609fb66eb88e","target":"record","created_at":"2026-07-05T10:22:47Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"e17f94cae2e872535820d388fc39916574d4f1eb548488ca6e9958d95ace0f7c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-07-03T16:38:57Z","title_canon_sha256":"71d8f141e62eb7cacf228e4c2b85dbeee161c1090cc38ca6a7f1d22c92da788d"},"schema_version":"1.0","source":{"id":"2407.03257","kind":"arxiv","version":2}},"canonical_sha256":"f8076405d32aa609c11921cfe5017bb95635272616c2700dcb8abe5ad1385634","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f8076405d32aa609c11921cfe5017bb95635272616c2700dcb8abe5ad1385634","first_computed_at":"2026-07-05T10:22:47.874133Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:22:47.874133Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"N0VqnAk+A6qIYCnqWpxcFvNEyxIeohqsAXGXjAx0pg5KSOkH6obAir4Pl5WfNoSrGGgHmjj+EarcDBp/FgqEDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:22:47.875069Z","signed_message":"canonical_sha256_bytes"},"source_id":"2407.03257","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:384b83ea6b00ac5e1a13293b3bcb8141e8e00a8ea3909ac187b4609fb66eb88e","sha256:bb0b9b3fa7b618c569b09546fb46d323731f983c3cc817873eabebf591f088bf"],"state_sha256":"b383ebc9f2a802f4c1fc933fe3e067c2fc4f8d228209ef7d4d5210af30d619e2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nMdKWuaneKeB8yuqrtjbJAK6Pp/FRCpe8YPuDVjK9tqVPkrQ36fzeD2JFr1S6cCyBLKeR5axjp2eBI44TczFDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T05:08:41.776802Z","bundle_sha256":"06a29e7b4d4256e92b80ec0c2673a049c02372073fdb0e5744852f5d88b0a907"}}