{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:DS5NJI3VYRY7O6Y4JYJLJBOTM2","short_pith_number":"pith:DS5NJI3V","schema_version":"1.0","canonical_sha256":"1cbad4a375c471f77b1c4e12b485d3668dda780ddd2aa29efc6c83bf4c413d13","source":{"kind":"arxiv","id":"2412.06195","version":1},"attestation_state":"computed","paper":{"title":"Adaptive Resolution Residual Networks -- Generalizing Across Resolutions Easily and Efficiently","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Glen Berseth, L\\'ea Demeule, Mahtab Sandhu","submitted_at":"2024-12-09T04:25:37Z","abstract_excerpt":"The majority of signal data captured in the real world uses numerous sensors with different resolutions. In practice, however, most deep learning architectures are fixed-resolution; they consider a single resolution at training time and inference time. This is convenient to implement but fails to fully take advantage of the diverse signal data that exists. In contrast, other deep learning architectures are adaptive-resolution; they directly allow various resolutions to be processed at training time and inference time. This benefits robustness and computational efficiency but introduces difficu"},"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":"2412.06195","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-09T04:25:37Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"5dfacdfaae09a69951f77aefbe37968212e7e7b5bd31350af4729653be70d759","abstract_canon_sha256":"63234f74be81e6deeeea57e9469337e500edbade963a541b0d1f90d620956974"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:46:16.960031Z","signature_b64":"ZYsiFgiPZ0uqeq7ky4gdylcoUU9iEe8iuAM2svT5c7t2lywhNq80JzW+mmZenC3tHIhXCdWNAb7Ag444MGD4AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1cbad4a375c471f77b1c4e12b485d3668dda780ddd2aa29efc6c83bf4c413d13","last_reissued_at":"2026-07-05T09:46:16.959644Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:46:16.959644Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adaptive Resolution Residual Networks -- Generalizing Across Resolutions Easily and Efficiently","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Glen Berseth, L\\'ea Demeule, Mahtab Sandhu","submitted_at":"2024-12-09T04:25:37Z","abstract_excerpt":"The majority of signal data captured in the real world uses numerous sensors with different resolutions. In practice, however, most deep learning architectures are fixed-resolution; they consider a single resolution at training time and inference time. This is convenient to implement but fails to fully take advantage of the diverse signal data that exists. In contrast, other deep learning architectures are adaptive-resolution; they directly allow various resolutions to be processed at training time and inference time. This benefits robustness and computational efficiency but introduces difficu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.06195","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/2412.06195/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":"2412.06195","created_at":"2026-07-05T09:46:16.959699+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.06195v1","created_at":"2026-07-05T09:46:16.959699+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.06195","created_at":"2026-07-05T09:46:16.959699+00:00"},{"alias_kind":"pith_short_12","alias_value":"DS5NJI3VYRY7","created_at":"2026-07-05T09:46:16.959699+00:00"},{"alias_kind":"pith_short_16","alias_value":"DS5NJI3VYRY7O6Y4","created_at":"2026-07-05T09:46:16.959699+00:00"},{"alias_kind":"pith_short_8","alias_value":"DS5NJI3V","created_at":"2026-07-05T09:46:16.959699+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/DS5NJI3VYRY7O6Y4JYJLJBOTM2","json":"https://pith.science/pith/DS5NJI3VYRY7O6Y4JYJLJBOTM2.json","graph_json":"https://pith.science/api/pith-number/DS5NJI3VYRY7O6Y4JYJLJBOTM2/graph.json","events_json":"https://pith.science/api/pith-number/DS5NJI3VYRY7O6Y4JYJLJBOTM2/events.json","paper":"https://pith.science/paper/DS5NJI3V"},"agent_actions":{"view_html":"https://pith.science/pith/DS5NJI3VYRY7O6Y4JYJLJBOTM2","download_json":"https://pith.science/pith/DS5NJI3VYRY7O6Y4JYJLJBOTM2.json","view_paper":"https://pith.science/paper/DS5NJI3V","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.06195&json=true","fetch_graph":"https://pith.science/api/pith-number/DS5NJI3VYRY7O6Y4JYJLJBOTM2/graph.json","fetch_events":"https://pith.science/api/pith-number/DS5NJI3VYRY7O6Y4JYJLJBOTM2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DS5NJI3VYRY7O6Y4JYJLJBOTM2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DS5NJI3VYRY7O6Y4JYJLJBOTM2/action/storage_attestation","attest_author":"https://pith.science/pith/DS5NJI3VYRY7O6Y4JYJLJBOTM2/action/author_attestation","sign_citation":"https://pith.science/pith/DS5NJI3VYRY7O6Y4JYJLJBOTM2/action/citation_signature","submit_replication":"https://pith.science/pith/DS5NJI3VYRY7O6Y4JYJLJBOTM2/action/replication_record"}},"created_at":"2026-07-05T09:46:16.959699+00:00","updated_at":"2026-07-05T09:46:16.959699+00:00"}