{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:FM5EGWAXL7TVFOUVZM6PDCCHTI","short_pith_number":"pith:FM5EGWAX","canonical_record":{"source":{"id":"2207.03554","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-07-07T20:03:32Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"5017f7b4cb1fd8bd3db68f0ee3df081c4726c319cc26a53b23b2c6827aa0cde6","abstract_canon_sha256":"786859b519ae2844788e51acf522df01bcd75f9dab3d3dd247b7648f929c925c"},"schema_version":"1.0"},"canonical_sha256":"2b3a4358175fe752ba95cb3cf188479a0608867de17357bd3c8539cc2994b11f","source":{"kind":"arxiv","id":"2207.03554","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.03554","created_at":"2026-07-05T04:38:25Z"},{"alias_kind":"arxiv_version","alias_value":"2207.03554v1","created_at":"2026-07-05T04:38:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.03554","created_at":"2026-07-05T04:38:25Z"},{"alias_kind":"pith_short_12","alias_value":"FM5EGWAXL7TV","created_at":"2026-07-05T04:38:25Z"},{"alias_kind":"pith_short_16","alias_value":"FM5EGWAXL7TVFOUV","created_at":"2026-07-05T04:38:25Z"},{"alias_kind":"pith_short_8","alias_value":"FM5EGWAX","created_at":"2026-07-05T04:38:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:FM5EGWAXL7TVFOUVZM6PDCCHTI","target":"record","payload":{"canonical_record":{"source":{"id":"2207.03554","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-07-07T20:03:32Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"5017f7b4cb1fd8bd3db68f0ee3df081c4726c319cc26a53b23b2c6827aa0cde6","abstract_canon_sha256":"786859b519ae2844788e51acf522df01bcd75f9dab3d3dd247b7648f929c925c"},"schema_version":"1.0"},"canonical_sha256":"2b3a4358175fe752ba95cb3cf188479a0608867de17357bd3c8539cc2994b11f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:38:25.754801Z","signature_b64":"23q/E8qvmAH5nhiNQwnvw1DjdJVFfGDqYygx33p/WdEUXo2pbGD+nXK8F1sVUELciC4bHC5OhGoP8nFW7477AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2b3a4358175fe752ba95cb3cf188479a0608867de17357bd3c8539cc2994b11f","last_reissued_at":"2026-07-05T04:38:25.754390Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:38:25.754390Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2207.03554","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:38:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UOM8PqoyUupO+K7Tt6G1rhwg/wSeel8SGwDtGxBzq60FUAnBZeReCVIZAJvTTI5MHFWIWPRwBqK/67ZEUNfsAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T05:05:42.614616Z"},"content_sha256":"cfc85f097c0729c5aa6e470c3cd949200ad55be5aba392bd6443fc9f97e2a418","schema_version":"1.0","event_id":"sha256:cfc85f097c0729c5aa6e470c3cd949200ad55be5aba392bd6443fc9f97e2a418"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:FM5EGWAXL7TVFOUVZM6PDCCHTI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"G2L: A Geometric Approach for Generating Pseudo-labels that Improve Transfer Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Bishwaranjan Bhattacharjee, Brian Belgodere, John R. Kender, Parijat Dube","submitted_at":"2022-07-07T20:03:32Z","abstract_excerpt":"Transfer learning is a deep-learning technique that ameliorates the problem of learning when human-annotated labels are expensive and limited. In place of such labels, it uses instead the previously trained weights from a well-chosen source model as the initial weights for the training of a base model for a new target dataset. We demonstrate a novel but general technique for automatically creating such source models. We generate pseudo-labels according to an efficient and extensible algorithm that is based on a classical result from the geometry of high dimensions, the Cayley-Menger determinan"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.03554","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/2207.03554/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":"2026-07-05T04:38:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oxq+2o7m7nM1SVuRNsJFLosoJzv23w0GHpMkxV1yFnKEXpdkUMSXmhLX5aRKNbkPmyMfbkb5BH8z7M7Ya2HeDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T05:05:42.614994Z"},"content_sha256":"44785f4073474d4410a9bbc4ff6448dc59a91143512450c9941e251c4ebf602d","schema_version":"1.0","event_id":"sha256:44785f4073474d4410a9bbc4ff6448dc59a91143512450c9941e251c4ebf602d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FM5EGWAXL7TVFOUVZM6PDCCHTI/bundle.json","state_url":"https://pith.science/pith/FM5EGWAXL7TVFOUVZM6PDCCHTI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FM5EGWAXL7TVFOUVZM6PDCCHTI/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":"2026-07-07T05:05:42Z","links":{"resolver":"https://pith.science/pith/FM5EGWAXL7TVFOUVZM6PDCCHTI","bundle":"https://pith.science/pith/FM5EGWAXL7TVFOUVZM6PDCCHTI/bundle.json","state":"https://pith.science/pith/FM5EGWAXL7TVFOUVZM6PDCCHTI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FM5EGWAXL7TVFOUVZM6PDCCHTI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:FM5EGWAXL7TVFOUVZM6PDCCHTI","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":"786859b519ae2844788e51acf522df01bcd75f9dab3d3dd247b7648f929c925c","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-07-07T20:03:32Z","title_canon_sha256":"5017f7b4cb1fd8bd3db68f0ee3df081c4726c319cc26a53b23b2c6827aa0cde6"},"schema_version":"1.0","source":{"id":"2207.03554","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.03554","created_at":"2026-07-05T04:38:25Z"},{"alias_kind":"arxiv_version","alias_value":"2207.03554v1","created_at":"2026-07-05T04:38:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.03554","created_at":"2026-07-05T04:38:25Z"},{"alias_kind":"pith_short_12","alias_value":"FM5EGWAXL7TV","created_at":"2026-07-05T04:38:25Z"},{"alias_kind":"pith_short_16","alias_value":"FM5EGWAXL7TVFOUV","created_at":"2026-07-05T04:38:25Z"},{"alias_kind":"pith_short_8","alias_value":"FM5EGWAX","created_at":"2026-07-05T04:38:25Z"}],"graph_snapshots":[{"event_id":"sha256:44785f4073474d4410a9bbc4ff6448dc59a91143512450c9941e251c4ebf602d","target":"graph","created_at":"2026-07-05T04:38:25Z","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/2207.03554/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Transfer learning is a deep-learning technique that ameliorates the problem of learning when human-annotated labels are expensive and limited. In place of such labels, it uses instead the previously trained weights from a well-chosen source model as the initial weights for the training of a base model for a new target dataset. We demonstrate a novel but general technique for automatically creating such source models. We generate pseudo-labels according to an efficient and extensible algorithm that is based on a classical result from the geometry of high dimensions, the Cayley-Menger determinan","authors_text":"Bishwaranjan Bhattacharjee, Brian Belgodere, John R. Kender, Parijat Dube","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-07-07T20:03:32Z","title":"G2L: A Geometric Approach for Generating Pseudo-labels that Improve Transfer Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.03554","kind":"arxiv","version":1},"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:cfc85f097c0729c5aa6e470c3cd949200ad55be5aba392bd6443fc9f97e2a418","target":"record","created_at":"2026-07-05T04:38:25Z","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":"786859b519ae2844788e51acf522df01bcd75f9dab3d3dd247b7648f929c925c","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-07-07T20:03:32Z","title_canon_sha256":"5017f7b4cb1fd8bd3db68f0ee3df081c4726c319cc26a53b23b2c6827aa0cde6"},"schema_version":"1.0","source":{"id":"2207.03554","kind":"arxiv","version":1}},"canonical_sha256":"2b3a4358175fe752ba95cb3cf188479a0608867de17357bd3c8539cc2994b11f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2b3a4358175fe752ba95cb3cf188479a0608867de17357bd3c8539cc2994b11f","first_computed_at":"2026-07-05T04:38:25.754390Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:38:25.754390Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"23q/E8qvmAH5nhiNQwnvw1DjdJVFfGDqYygx33p/WdEUXo2pbGD+nXK8F1sVUELciC4bHC5OhGoP8nFW7477AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:38:25.754801Z","signed_message":"canonical_sha256_bytes"},"source_id":"2207.03554","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cfc85f097c0729c5aa6e470c3cd949200ad55be5aba392bd6443fc9f97e2a418","sha256:44785f4073474d4410a9bbc4ff6448dc59a91143512450c9941e251c4ebf602d"],"state_sha256":"0c9a204786913193a67c941348dae32106500d1fa0941b589ef4ea04198971e2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+AjRsyenFnzFpiK/Y2hvlHWIOvG10en+dONCbHVz7O+2HW79htf3k94HxFwfF7QPpp2WU6KBSmbF6kKgwiI/Cg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T05:05:42.617502Z","bundle_sha256":"7c2315944eca18b2f93558e52f052260f7a2f85dcab760c4b41d3b880a1689a2"}}