{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:MRIU5ZAFD36MMPQAQRAISAZEH2","short_pith_number":"pith:MRIU5ZAF","schema_version":"1.0","canonical_sha256":"64514ee4051efcc63e0084408903243eaa8de61c72769fe15a69b4ce43dda8a5","source":{"kind":"arxiv","id":"1605.01636","version":2},"attestation_state":"computed","paper":{"title":"Maximal Sparsity with Deep Networks?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Bo Xin, David Wipf, Wen Gao, Yizhou Wang","submitted_at":"2016-05-05T15:58:55Z","abstract_excerpt":"The iterations of many sparse estimation algorithms are comprised of a fixed linear filter cascaded with a thresholding nonlinearity, which collectively resemble a typical neural network layer. Consequently, a lengthy sequence of algorithm iterations can be viewed as a deep network with shared, hand-crafted layer weights. It is therefore quite natural to examine the degree to which a learned network model might act as a viable surrogate for traditional sparse estimation in domains where ample training data is available. While the possibility of a reduced computational budget is readily apparen"},"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":"1605.01636","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-05-05T15:58:55Z","cross_cats_sorted":[],"title_canon_sha256":"2a01464255ddce344de6eff6cd5c87bde8dc98e19625c7cf681f2a5e819157ca","abstract_canon_sha256":"16740ad6b740d24d966eaf90f239a01e1993faed688f782b116e5a95c46bd072"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:15:13.324683Z","signature_b64":"1+GRcG8CDdY/smw+FMLxd0aJTEk4JE8ngvlASykc2II/WS62fUxVlbMQY9pApE5Pht7Ff/yI525mUHF3koEtCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"64514ee4051efcc63e0084408903243eaa8de61c72769fe15a69b4ce43dda8a5","last_reissued_at":"2026-05-18T01:15:13.324165Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:15:13.324165Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Maximal Sparsity with Deep Networks?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Bo Xin, David Wipf, Wen Gao, Yizhou Wang","submitted_at":"2016-05-05T15:58:55Z","abstract_excerpt":"The iterations of many sparse estimation algorithms are comprised of a fixed linear filter cascaded with a thresholding nonlinearity, which collectively resemble a typical neural network layer. Consequently, a lengthy sequence of algorithm iterations can be viewed as a deep network with shared, hand-crafted layer weights. It is therefore quite natural to examine the degree to which a learned network model might act as a viable surrogate for traditional sparse estimation in domains where ample training data is available. While the possibility of a reduced computational budget is readily apparen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1605.01636","kind":"arxiv","version":2},"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":"1605.01636","created_at":"2026-05-18T01:15:13.324241+00:00"},{"alias_kind":"arxiv_version","alias_value":"1605.01636v2","created_at":"2026-05-18T01:15:13.324241+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1605.01636","created_at":"2026-05-18T01:15:13.324241+00:00"},{"alias_kind":"pith_short_12","alias_value":"MRIU5ZAFD36M","created_at":"2026-05-18T12:30:32.724797+00:00"},{"alias_kind":"pith_short_16","alias_value":"MRIU5ZAFD36MMPQA","created_at":"2026-05-18T12:30:32.724797+00:00"},{"alias_kind":"pith_short_8","alias_value":"MRIU5ZAF","created_at":"2026-05-18T12:30:32.724797+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/MRIU5ZAFD36MMPQAQRAISAZEH2","json":"https://pith.science/pith/MRIU5ZAFD36MMPQAQRAISAZEH2.json","graph_json":"https://pith.science/api/pith-number/MRIU5ZAFD36MMPQAQRAISAZEH2/graph.json","events_json":"https://pith.science/api/pith-number/MRIU5ZAFD36MMPQAQRAISAZEH2/events.json","paper":"https://pith.science/paper/MRIU5ZAF"},"agent_actions":{"view_html":"https://pith.science/pith/MRIU5ZAFD36MMPQAQRAISAZEH2","download_json":"https://pith.science/pith/MRIU5ZAFD36MMPQAQRAISAZEH2.json","view_paper":"https://pith.science/paper/MRIU5ZAF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1605.01636&json=true","fetch_graph":"https://pith.science/api/pith-number/MRIU5ZAFD36MMPQAQRAISAZEH2/graph.json","fetch_events":"https://pith.science/api/pith-number/MRIU5ZAFD36MMPQAQRAISAZEH2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MRIU5ZAFD36MMPQAQRAISAZEH2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MRIU5ZAFD36MMPQAQRAISAZEH2/action/storage_attestation","attest_author":"https://pith.science/pith/MRIU5ZAFD36MMPQAQRAISAZEH2/action/author_attestation","sign_citation":"https://pith.science/pith/MRIU5ZAFD36MMPQAQRAISAZEH2/action/citation_signature","submit_replication":"https://pith.science/pith/MRIU5ZAFD36MMPQAQRAISAZEH2/action/replication_record"}},"created_at":"2026-05-18T01:15:13.324241+00:00","updated_at":"2026-05-18T01:15:13.324241+00:00"}