{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:OQSYSCX6I7ZBXVNLRUZILKT5IY","short_pith_number":"pith:OQSYSCX6","schema_version":"1.0","canonical_sha256":"7425890afe47f21bd5ab8d3285aa7d4606bc122efe97a74f11622777a0f901ec","source":{"kind":"arxiv","id":"2210.13103","version":1},"attestation_state":"computed","paper":{"title":"Deep Grey-Box Modeling With Adaptive Data-Driven Models Toward Trustworthy Estimation of Theory-Driven Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Alexandros Kalousis, Naoya Takeishi","submitted_at":"2022-10-24T10:42:26Z","abstract_excerpt":"The combination of deep neural nets and theory-driven models, which we call deep grey-box modeling, can be inherently interpretable to some extent thanks to the theory backbone. Deep grey-box models are usually learned with a regularized risk minimization to prevent a theory-driven part from being overwritten and ignored by a deep neural net. However, an estimation of the theory-driven part obtained by uncritically optimizing a regularizer can hardly be trustworthy when we are not sure what regularizer is suitable for the given data, which may harm the interpretability. Toward a trustworthy es"},"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":"2210.13103","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-10-24T10:42:26Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"4ed50a337e4bf552dd5d49a65e0d0554b2c5273d11dac86b2a0c7dda13cd478a","abstract_canon_sha256":"1f53f22149fb906e9154f2b46532f315e6b1fdf9a0c3e8208dab1179405c4dcb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:09:36.289452Z","signature_b64":"5XQqiBuTUqRoXWlF3jnf4h8z5gE8vYSZeUCuMW/QwaUj1QfcRi6wMawCljN8dC+QPgigYR+6/KZG28C51obqDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7425890afe47f21bd5ab8d3285aa7d4606bc122efe97a74f11622777a0f901ec","last_reissued_at":"2026-07-05T05:09:36.289015Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:09:36.289015Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Grey-Box Modeling With Adaptive Data-Driven Models Toward Trustworthy Estimation of Theory-Driven Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Alexandros Kalousis, Naoya Takeishi","submitted_at":"2022-10-24T10:42:26Z","abstract_excerpt":"The combination of deep neural nets and theory-driven models, which we call deep grey-box modeling, can be inherently interpretable to some extent thanks to the theory backbone. Deep grey-box models are usually learned with a regularized risk minimization to prevent a theory-driven part from being overwritten and ignored by a deep neural net. However, an estimation of the theory-driven part obtained by uncritically optimizing a regularizer can hardly be trustworthy when we are not sure what regularizer is suitable for the given data, which may harm the interpretability. Toward a trustworthy es"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.13103","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/2210.13103/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":"2210.13103","created_at":"2026-07-05T05:09:36.289106+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.13103v1","created_at":"2026-07-05T05:09:36.289106+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.13103","created_at":"2026-07-05T05:09:36.289106+00:00"},{"alias_kind":"pith_short_12","alias_value":"OQSYSCX6I7ZB","created_at":"2026-07-05T05:09:36.289106+00:00"},{"alias_kind":"pith_short_16","alias_value":"OQSYSCX6I7ZBXVNL","created_at":"2026-07-05T05:09:36.289106+00:00"},{"alias_kind":"pith_short_8","alias_value":"OQSYSCX6","created_at":"2026-07-05T05:09:36.289106+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.00571","citing_title":"Gray-Box Computed Torque Control for Differential-Drive Mobile Robot Tracking","ref_index":26,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OQSYSCX6I7ZBXVNLRUZILKT5IY","json":"https://pith.science/pith/OQSYSCX6I7ZBXVNLRUZILKT5IY.json","graph_json":"https://pith.science/api/pith-number/OQSYSCX6I7ZBXVNLRUZILKT5IY/graph.json","events_json":"https://pith.science/api/pith-number/OQSYSCX6I7ZBXVNLRUZILKT5IY/events.json","paper":"https://pith.science/paper/OQSYSCX6"},"agent_actions":{"view_html":"https://pith.science/pith/OQSYSCX6I7ZBXVNLRUZILKT5IY","download_json":"https://pith.science/pith/OQSYSCX6I7ZBXVNLRUZILKT5IY.json","view_paper":"https://pith.science/paper/OQSYSCX6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.13103&json=true","fetch_graph":"https://pith.science/api/pith-number/OQSYSCX6I7ZBXVNLRUZILKT5IY/graph.json","fetch_events":"https://pith.science/api/pith-number/OQSYSCX6I7ZBXVNLRUZILKT5IY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OQSYSCX6I7ZBXVNLRUZILKT5IY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OQSYSCX6I7ZBXVNLRUZILKT5IY/action/storage_attestation","attest_author":"https://pith.science/pith/OQSYSCX6I7ZBXVNLRUZILKT5IY/action/author_attestation","sign_citation":"https://pith.science/pith/OQSYSCX6I7ZBXVNLRUZILKT5IY/action/citation_signature","submit_replication":"https://pith.science/pith/OQSYSCX6I7ZBXVNLRUZILKT5IY/action/replication_record"}},"created_at":"2026-07-05T05:09:36.289106+00:00","updated_at":"2026-07-05T05:09:36.289106+00:00"}