{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:NFLNNV76R3CD6DASSL3O63Y32X","short_pith_number":"pith:NFLNNV76","schema_version":"1.0","canonical_sha256":"6956d6d7fe8ec43f0c1292f6ef6f1bd5df299e17bbca42ef56a4953ba78ac683","source":{"kind":"arxiv","id":"1711.09256","version":1},"attestation_state":"computed","paper":{"title":"Expectation maximization transfer learning and its application for bionic hand prostheses","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alexander Schulz, Barbara Hammer, Benjamin Paa{\\ss}en, Janne Hahne","submitted_at":"2017-11-25T16:04:07Z","abstract_excerpt":"Machine learning models in practical settings are typically confronted with changes to the distribution of the incoming data. Such changes can severely affect the model performance, leading for example to misclassifications of data. This is particularly apparent in the domain of bionic hand prostheses, where machine learning models promise faster and more intuitive user interfaces, but are hindered by their lack of robustness to everyday disturbances, such as electrode shifts. One way to address changes in the data distribution is transfer learning, that is, to transfer the disturbed data to a"},"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":"1711.09256","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-25T16:04:07Z","cross_cats_sorted":[],"title_canon_sha256":"2d2a28ef92c0be8001c441e7b1fb8b6220fa23f41fb1242766cc5c97ef01b0bd","abstract_canon_sha256":"af2996df05872b5e308fc57838cbafe63100d32feb1a880794dda62ebefaddb9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:17:33.032711Z","signature_b64":"mLieo8XGz+zJitD1ge/40cAuonnfUtHuB7X4z+HPlV56Tnn48qRImIYYiBktQP4lKQFLSvJ6XIDixMlcQDbzCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6956d6d7fe8ec43f0c1292f6ef6f1bd5df299e17bbca42ef56a4953ba78ac683","last_reissued_at":"2026-05-18T00:17:33.032086Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:17:33.032086Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Expectation maximization transfer learning and its application for bionic hand prostheses","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alexander Schulz, Barbara Hammer, Benjamin Paa{\\ss}en, Janne Hahne","submitted_at":"2017-11-25T16:04:07Z","abstract_excerpt":"Machine learning models in practical settings are typically confronted with changes to the distribution of the incoming data. Such changes can severely affect the model performance, leading for example to misclassifications of data. This is particularly apparent in the domain of bionic hand prostheses, where machine learning models promise faster and more intuitive user interfaces, but are hindered by their lack of robustness to everyday disturbances, such as electrode shifts. One way to address changes in the data distribution is transfer learning, that is, to transfer the disturbed data to a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.09256","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1711.09256","created_at":"2026-05-18T00:17:33.032179+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.09256v1","created_at":"2026-05-18T00:17:33.032179+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.09256","created_at":"2026-05-18T00:17:33.032179+00:00"},{"alias_kind":"pith_short_12","alias_value":"NFLNNV76R3CD","created_at":"2026-05-18T12:31:31.346846+00:00"},{"alias_kind":"pith_short_16","alias_value":"NFLNNV76R3CD6DAS","created_at":"2026-05-18T12:31:31.346846+00:00"},{"alias_kind":"pith_short_8","alias_value":"NFLNNV76","created_at":"2026-05-18T12:31:31.346846+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/NFLNNV76R3CD6DASSL3O63Y32X","json":"https://pith.science/pith/NFLNNV76R3CD6DASSL3O63Y32X.json","graph_json":"https://pith.science/api/pith-number/NFLNNV76R3CD6DASSL3O63Y32X/graph.json","events_json":"https://pith.science/api/pith-number/NFLNNV76R3CD6DASSL3O63Y32X/events.json","paper":"https://pith.science/paper/NFLNNV76"},"agent_actions":{"view_html":"https://pith.science/pith/NFLNNV76R3CD6DASSL3O63Y32X","download_json":"https://pith.science/pith/NFLNNV76R3CD6DASSL3O63Y32X.json","view_paper":"https://pith.science/paper/NFLNNV76","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.09256&json=true","fetch_graph":"https://pith.science/api/pith-number/NFLNNV76R3CD6DASSL3O63Y32X/graph.json","fetch_events":"https://pith.science/api/pith-number/NFLNNV76R3CD6DASSL3O63Y32X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NFLNNV76R3CD6DASSL3O63Y32X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NFLNNV76R3CD6DASSL3O63Y32X/action/storage_attestation","attest_author":"https://pith.science/pith/NFLNNV76R3CD6DASSL3O63Y32X/action/author_attestation","sign_citation":"https://pith.science/pith/NFLNNV76R3CD6DASSL3O63Y32X/action/citation_signature","submit_replication":"https://pith.science/pith/NFLNNV76R3CD6DASSL3O63Y32X/action/replication_record"}},"created_at":"2026-05-18T00:17:33.032179+00:00","updated_at":"2026-05-18T00:17:33.032179+00:00"}