{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:OQ6743IMQO6J3Q3II3AK22I7HC","short_pith_number":"pith:OQ6743IM","canonical_record":{"source":{"id":"1812.01063","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-03T20:15:05Z","cross_cats_sorted":["cs.AI","cs.CV","stat.ML"],"title_canon_sha256":"b872c703a557b5dfc42b147291813595f22c303845e572248d738dd7fefc37cd","abstract_canon_sha256":"dc6a8c2aca34617994ae5ee24ddbf936dcf32c6d71af78d257e9230ee3aa1740"},"schema_version":"1.0"},"canonical_sha256":"743dfe6d0c83bc9dc36846c0ad691f38bac6559ac6442555918a22d3e6c73dcf","source":{"kind":"arxiv","id":"1812.01063","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.01063","created_at":"2026-05-17T23:59:13Z"},{"alias_kind":"arxiv_version","alias_value":"1812.01063v1","created_at":"2026-05-17T23:59:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.01063","created_at":"2026-05-17T23:59:13Z"},{"alias_kind":"pith_short_12","alias_value":"OQ6743IMQO6J","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"OQ6743IMQO6J3Q3I","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"OQ6743IM","created_at":"2026-05-18T12:32:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:OQ6743IMQO6J3Q3II3AK22I7HC","target":"record","payload":{"canonical_record":{"source":{"id":"1812.01063","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-03T20:15:05Z","cross_cats_sorted":["cs.AI","cs.CV","stat.ML"],"title_canon_sha256":"b872c703a557b5dfc42b147291813595f22c303845e572248d738dd7fefc37cd","abstract_canon_sha256":"dc6a8c2aca34617994ae5ee24ddbf936dcf32c6d71af78d257e9230ee3aa1740"},"schema_version":"1.0"},"canonical_sha256":"743dfe6d0c83bc9dc36846c0ad691f38bac6559ac6442555918a22d3e6c73dcf","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:13.402676Z","signature_b64":"2s7+pJQrZLJ32ZDnZU6N8jZwYzMkx5t10bR5yd6O2NWRb0q+tiYecOMbwh3vW0KZNT8gK2l/yFP0bvoj700zCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"743dfe6d0c83bc9dc36846c0ad691f38bac6559ac6442555918a22d3e6c73dcf","last_reissued_at":"2026-05-17T23:59:13.402104Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:13.402104Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1812.01063","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":"2026-05-17T23:59:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pTUKW2WIOEH5QCb0rI42S0XeGikTCcMRnYXngt4dAE5RbJaVHFQ6gY2Ep/A3F1dj9ZY97VZkKdITGCUzBowqCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T01:33:17.696707Z"},"content_sha256":"d6d8f459f9be92adf740c28dcc91ef77db03be84c10e640ede920fc17c5c1c3b","schema_version":"1.0","event_id":"sha256:d6d8f459f9be92adf740c28dcc91ef77db03be84c10e640ede920fc17c5c1c3b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:OQ6743IMQO6J3Q3II3AK22I7HC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Hybrid Instance-based Transfer Learning Method","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Ahmed Bilal Ashraf, Ariel Sibilia, Azin Asgarian, Babak Taati, Ji Chao Zhang, Madalin Mihailescu, Parinaz Sobhani","submitted_at":"2018-12-03T20:15:05Z","abstract_excerpt":"In recent years, supervised machine learning models have demonstrated tremendous success in a variety of application domains. Despite the promising results, these successful models are data hungry and their performance relies heavily on the size of training data. However, in many healthcare applications it is difficult to collect sufficiently large training datasets. Transfer learning can help overcome this issue by transferring the knowledge from readily available datasets (source) to a new dataset (target). In this work, we propose a hybrid instance-based transfer learning method that outper"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.01063","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":""},"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":"2026-05-17T23:59:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VFqVNyxYNzlNXwWFQqqWvniqHVkdmKRnowHEP2rM6/l8oeEDBQ6OtjCkhBukd9GA+8vVy79VvbdVZodV6L7tAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T01:33:17.697376Z"},"content_sha256":"186c5711aeedc8647035a0e708cd7372458629101a2f42643fc01bfeb6e45c11","schema_version":"1.0","event_id":"sha256:186c5711aeedc8647035a0e708cd7372458629101a2f42643fc01bfeb6e45c11"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OQ6743IMQO6J3Q3II3AK22I7HC/bundle.json","state_url":"https://pith.science/pith/OQ6743IMQO6J3Q3II3AK22I7HC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OQ6743IMQO6J3Q3II3AK22I7HC/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":"2026-05-27T01:33:17Z","links":{"resolver":"https://pith.science/pith/OQ6743IMQO6J3Q3II3AK22I7HC","bundle":"https://pith.science/pith/OQ6743IMQO6J3Q3II3AK22I7HC/bundle.json","state":"https://pith.science/pith/OQ6743IMQO6J3Q3II3AK22I7HC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OQ6743IMQO6J3Q3II3AK22I7HC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:OQ6743IMQO6J3Q3II3AK22I7HC","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":"dc6a8c2aca34617994ae5ee24ddbf936dcf32c6d71af78d257e9230ee3aa1740","cross_cats_sorted":["cs.AI","cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-03T20:15:05Z","title_canon_sha256":"b872c703a557b5dfc42b147291813595f22c303845e572248d738dd7fefc37cd"},"schema_version":"1.0","source":{"id":"1812.01063","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.01063","created_at":"2026-05-17T23:59:13Z"},{"alias_kind":"arxiv_version","alias_value":"1812.01063v1","created_at":"2026-05-17T23:59:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.01063","created_at":"2026-05-17T23:59:13Z"},{"alias_kind":"pith_short_12","alias_value":"OQ6743IMQO6J","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"OQ6743IMQO6J3Q3I","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"OQ6743IM","created_at":"2026-05-18T12:32:43Z"}],"graph_snapshots":[{"event_id":"sha256:186c5711aeedc8647035a0e708cd7372458629101a2f42643fc01bfeb6e45c11","target":"graph","created_at":"2026-05-17T23:59:13Z","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"},"paper":{"abstract_excerpt":"In recent years, supervised machine learning models have demonstrated tremendous success in a variety of application domains. Despite the promising results, these successful models are data hungry and their performance relies heavily on the size of training data. However, in many healthcare applications it is difficult to collect sufficiently large training datasets. Transfer learning can help overcome this issue by transferring the knowledge from readily available datasets (source) to a new dataset (target). In this work, we propose a hybrid instance-based transfer learning method that outper","authors_text":"Ahmed Bilal Ashraf, Ariel Sibilia, Azin Asgarian, Babak Taati, Ji Chao Zhang, Madalin Mihailescu, Parinaz Sobhani","cross_cats":["cs.AI","cs.CV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-03T20:15:05Z","title":"A Hybrid Instance-based Transfer Learning Method"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.01063","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:d6d8f459f9be92adf740c28dcc91ef77db03be84c10e640ede920fc17c5c1c3b","target":"record","created_at":"2026-05-17T23:59:13Z","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":"dc6a8c2aca34617994ae5ee24ddbf936dcf32c6d71af78d257e9230ee3aa1740","cross_cats_sorted":["cs.AI","cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-03T20:15:05Z","title_canon_sha256":"b872c703a557b5dfc42b147291813595f22c303845e572248d738dd7fefc37cd"},"schema_version":"1.0","source":{"id":"1812.01063","kind":"arxiv","version":1}},"canonical_sha256":"743dfe6d0c83bc9dc36846c0ad691f38bac6559ac6442555918a22d3e6c73dcf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"743dfe6d0c83bc9dc36846c0ad691f38bac6559ac6442555918a22d3e6c73dcf","first_computed_at":"2026-05-17T23:59:13.402104Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:59:13.402104Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2s7+pJQrZLJ32ZDnZU6N8jZwYzMkx5t10bR5yd6O2NWRb0q+tiYecOMbwh3vW0KZNT8gK2l/yFP0bvoj700zCQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:59:13.402676Z","signed_message":"canonical_sha256_bytes"},"source_id":"1812.01063","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d6d8f459f9be92adf740c28dcc91ef77db03be84c10e640ede920fc17c5c1c3b","sha256:186c5711aeedc8647035a0e708cd7372458629101a2f42643fc01bfeb6e45c11"],"state_sha256":"eaa742fe986b6ecba6225c2f7381b3c3eed59dcd8e3d41ee4b2b62c728b1803e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+C00hWEYsyXTJXc6sMLZFw8GRsYiA2DMtN5i0XkQPWDQqzjdUbJj3D492b9zodMm9X5MRoET+tRpNHnVeIBYDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T01:33:17.700711Z","bundle_sha256":"9fc5624a1fa1e0246e562a1278fc629132422f57ea78d958a59f27a8a5c882af"}}