{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:HYCQ2STZVIBZOMCI6DSWZPDVI6","short_pith_number":"pith:HYCQ2STZ","schema_version":"1.0","canonical_sha256":"3e050d4a79aa03973048f0e56cbc7547949bf984f0eef9696dfc14921b585c4a","source":{"kind":"arxiv","id":"1909.02803","version":3},"attestation_state":"computed","paper":{"title":"Personalization of Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Johannes Schneider, Michail Vlachos","submitted_at":"2019-09-06T10:17:25Z","abstract_excerpt":"We discuss training techniques, objectives and metrics toward personalization of deep learning models. In machine learning, personalization addresses the goal of a trained model to target a particular individual by optimizing one or more performance metrics, while conforming to certain constraints. To personalize, we investigate three methods of ``curriculum learning`` and two approaches for data grouping, i.e., augmenting the data of an individual by adding similar data identified with an auto-encoder. We show that both ``curriculuum learning'' and ``personalized'' data augmentation lead to i"},"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":"1909.02803","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-06T10:17:25Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"98b4e608863ad07e57c4866229cbd80700296c7de6702a9f8cf2ead25f17b878","abstract_canon_sha256":"e8d451fb822c0e745c5d5a90c6613a253d6643b27369f170a1ae0e6263d5b816"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:46:48.501431Z","signature_b64":"eTW/PZ1i42SpCGwH0TKSB5lpdmdrtR5ap0vMzT0GACtACBS6bIF9dUic0pWeQBEEDZrEY+lN4PT8CljWSbuBBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3e050d4a79aa03973048f0e56cbc7547949bf984f0eef9696dfc14921b585c4a","last_reissued_at":"2026-07-05T00:46:48.500951Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:46:48.500951Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Personalization of Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Johannes Schneider, Michail Vlachos","submitted_at":"2019-09-06T10:17:25Z","abstract_excerpt":"We discuss training techniques, objectives and metrics toward personalization of deep learning models. In machine learning, personalization addresses the goal of a trained model to target a particular individual by optimizing one or more performance metrics, while conforming to certain constraints. To personalize, we investigate three methods of ``curriculum learning`` and two approaches for data grouping, i.e., augmenting the data of an individual by adding similar data identified with an auto-encoder. We show that both ``curriculuum learning'' and ``personalized'' data augmentation lead to i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.02803","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/1909.02803/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":"1909.02803","created_at":"2026-07-05T00:46:48.501008+00:00"},{"alias_kind":"arxiv_version","alias_value":"1909.02803v3","created_at":"2026-07-05T00:46:48.501008+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.02803","created_at":"2026-07-05T00:46:48.501008+00:00"},{"alias_kind":"pith_short_12","alias_value":"HYCQ2STZVIBZ","created_at":"2026-07-05T00:46:48.501008+00:00"},{"alias_kind":"pith_short_16","alias_value":"HYCQ2STZVIBZOMCI","created_at":"2026-07-05T00:46:48.501008+00:00"},{"alias_kind":"pith_short_8","alias_value":"HYCQ2STZ","created_at":"2026-07-05T00:46:48.501008+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2401.12783","citing_title":"A Scoping Review of Deep Learning Methods for Photoplethysmography Data","ref_index":271,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HYCQ2STZVIBZOMCI6DSWZPDVI6","json":"https://pith.science/pith/HYCQ2STZVIBZOMCI6DSWZPDVI6.json","graph_json":"https://pith.science/api/pith-number/HYCQ2STZVIBZOMCI6DSWZPDVI6/graph.json","events_json":"https://pith.science/api/pith-number/HYCQ2STZVIBZOMCI6DSWZPDVI6/events.json","paper":"https://pith.science/paper/HYCQ2STZ"},"agent_actions":{"view_html":"https://pith.science/pith/HYCQ2STZVIBZOMCI6DSWZPDVI6","download_json":"https://pith.science/pith/HYCQ2STZVIBZOMCI6DSWZPDVI6.json","view_paper":"https://pith.science/paper/HYCQ2STZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1909.02803&json=true","fetch_graph":"https://pith.science/api/pith-number/HYCQ2STZVIBZOMCI6DSWZPDVI6/graph.json","fetch_events":"https://pith.science/api/pith-number/HYCQ2STZVIBZOMCI6DSWZPDVI6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HYCQ2STZVIBZOMCI6DSWZPDVI6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HYCQ2STZVIBZOMCI6DSWZPDVI6/action/storage_attestation","attest_author":"https://pith.science/pith/HYCQ2STZVIBZOMCI6DSWZPDVI6/action/author_attestation","sign_citation":"https://pith.science/pith/HYCQ2STZVIBZOMCI6DSWZPDVI6/action/citation_signature","submit_replication":"https://pith.science/pith/HYCQ2STZVIBZOMCI6DSWZPDVI6/action/replication_record"}},"created_at":"2026-07-05T00:46:48.501008+00:00","updated_at":"2026-07-05T00:46:48.501008+00:00"}