{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:NJXH6NG7JFY4GRHPMRMLS5I6M4","short_pith_number":"pith:NJXH6NG7","schema_version":"1.0","canonical_sha256":"6a6e7f34df4971c344ef6458b9751e67079bc88b36b5e96eca39c9a3dd8373a1","source":{"kind":"arxiv","id":"2106.01143","version":1},"attestation_state":"computed","paper":{"title":"Accurate and Robust Deep Learning Framework for Solving Wave-Based Inverse Problems in the Super-Resolution Regime","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NA","stat.ML"],"primary_cat":"math.NA","authors_text":"Laurent Demanet, Leonardo Zepeda-N\\'u\\~nez, Matthew Li","submitted_at":"2021-06-02T13:30:28Z","abstract_excerpt":"We propose an end-to-end deep learning framework that comprehensively solves the inverse wave scattering problem across all length scales. Our framework consists of the newly introduced wide-band butterfly network coupled with a simple training procedure that dynamically injects noise during training. While our trained network provides competitive results in classical imaging regimes, most notably it also succeeds in the super-resolution regime where other comparable methods fail. This encompasses both (i) reconstruction of scatterers with sub-wavelength geometric features, and (ii) accurate 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":"2106.01143","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2021-06-02T13:30:28Z","cross_cats_sorted":["cs.LG","cs.NA","stat.ML"],"title_canon_sha256":"bf31893a07f2e1f69de20cb5ed917f62856369eb10bb890885918a1602511186","abstract_canon_sha256":"0d345daaba4e94631e379ec744cbbdb1e8036093b0fd48d4bca78fb71babeba2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:45:44.255273Z","signature_b64":"3/vQEiDNHwsQfN8rnS2KIXhYaS765E3YZ1rEuy0a3aPJq7+GuUjx42feVyuGsp1tHVPcmKrz2sFrg8NgmGwDBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6a6e7f34df4971c344ef6458b9751e67079bc88b36b5e96eca39c9a3dd8373a1","last_reissued_at":"2026-07-05T02:45:44.254781Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:45:44.254781Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Accurate and Robust Deep Learning Framework for Solving Wave-Based Inverse Problems in the Super-Resolution Regime","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NA","stat.ML"],"primary_cat":"math.NA","authors_text":"Laurent Demanet, Leonardo Zepeda-N\\'u\\~nez, Matthew Li","submitted_at":"2021-06-02T13:30:28Z","abstract_excerpt":"We propose an end-to-end deep learning framework that comprehensively solves the inverse wave scattering problem across all length scales. Our framework consists of the newly introduced wide-band butterfly network coupled with a simple training procedure that dynamically injects noise during training. While our trained network provides competitive results in classical imaging regimes, most notably it also succeeds in the super-resolution regime where other comparable methods fail. This encompasses both (i) reconstruction of scatterers with sub-wavelength geometric features, and (ii) accurate i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.01143","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/2106.01143/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":"2106.01143","created_at":"2026-07-05T02:45:44.254845+00:00"},{"alias_kind":"arxiv_version","alias_value":"2106.01143v1","created_at":"2026-07-05T02:45:44.254845+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.01143","created_at":"2026-07-05T02:45:44.254845+00:00"},{"alias_kind":"pith_short_12","alias_value":"NJXH6NG7JFY4","created_at":"2026-07-05T02:45:44.254845+00:00"},{"alias_kind":"pith_short_16","alias_value":"NJXH6NG7JFY4GRHP","created_at":"2026-07-05T02:45:44.254845+00:00"},{"alias_kind":"pith_short_8","alias_value":"NJXH6NG7","created_at":"2026-07-05T02:45:44.254845+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.03979","citing_title":"Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach","ref_index":180,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NJXH6NG7JFY4GRHPMRMLS5I6M4","json":"https://pith.science/pith/NJXH6NG7JFY4GRHPMRMLS5I6M4.json","graph_json":"https://pith.science/api/pith-number/NJXH6NG7JFY4GRHPMRMLS5I6M4/graph.json","events_json":"https://pith.science/api/pith-number/NJXH6NG7JFY4GRHPMRMLS5I6M4/events.json","paper":"https://pith.science/paper/NJXH6NG7"},"agent_actions":{"view_html":"https://pith.science/pith/NJXH6NG7JFY4GRHPMRMLS5I6M4","download_json":"https://pith.science/pith/NJXH6NG7JFY4GRHPMRMLS5I6M4.json","view_paper":"https://pith.science/paper/NJXH6NG7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2106.01143&json=true","fetch_graph":"https://pith.science/api/pith-number/NJXH6NG7JFY4GRHPMRMLS5I6M4/graph.json","fetch_events":"https://pith.science/api/pith-number/NJXH6NG7JFY4GRHPMRMLS5I6M4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NJXH6NG7JFY4GRHPMRMLS5I6M4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NJXH6NG7JFY4GRHPMRMLS5I6M4/action/storage_attestation","attest_author":"https://pith.science/pith/NJXH6NG7JFY4GRHPMRMLS5I6M4/action/author_attestation","sign_citation":"https://pith.science/pith/NJXH6NG7JFY4GRHPMRMLS5I6M4/action/citation_signature","submit_replication":"https://pith.science/pith/NJXH6NG7JFY4GRHPMRMLS5I6M4/action/replication_record"}},"created_at":"2026-07-05T02:45:44.254845+00:00","updated_at":"2026-07-05T02:45:44.254845+00:00"}