{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:X2M2C5A3WRBENBUKTDG5ANJRNF","short_pith_number":"pith:X2M2C5A3","schema_version":"1.0","canonical_sha256":"be99a1741bb44246868a98cdd035316972513a27f2fd745ecdf30f5003b0da3b","source":{"kind":"arxiv","id":"1709.00106","version":3},"attestation_state":"computed","paper":{"title":"First and Second Order Methods for Online Convolutional Dictionary Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","eess.IV","math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Brendt Wohlberg, Cristina Garcia-Cardona, Jialin Liu, Wotao Yin","submitted_at":"2017-08-31T23:19:02Z","abstract_excerpt":"Convolutional sparse representations are a form of sparse representation with a structured, translation invariant dictionary. Most convolutional dictionary learning algorithms to date operate in batch mode, requiring simultaneous access to all training images during the learning process, which results in very high memory usage and severely limits the training data that can be used. Very recently, however, a number of authors have considered the design of online convolutional dictionary learning algorithms that offer far better scaling of memory and computational cost with training set size tha"},"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":"1709.00106","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-08-31T23:19:02Z","cross_cats_sorted":["cs.CV","eess.IV","math.OC","stat.ML"],"title_canon_sha256":"5591c9670b534cdd909e40c768f863b5f4de8275a34037a147627a796d381b04","abstract_canon_sha256":"0dccac72c762585c33e741346da3e904324df870eb80014fad29166b974857b2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:13:07.447901Z","signature_b64":"pX6Nh0rxEon2zf107OJ0yEjJ5dReLf2XbUWXT34imo1SyKBBnfQS+XNtQqEdy8v3xaqR4ZdfNkICtPjW6N/5DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"be99a1741bb44246868a98cdd035316972513a27f2fd745ecdf30f5003b0da3b","last_reissued_at":"2026-05-18T00:13:07.447153Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:13:07.447153Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"First and Second Order Methods for Online Convolutional Dictionary Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","eess.IV","math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Brendt Wohlberg, Cristina Garcia-Cardona, Jialin Liu, Wotao Yin","submitted_at":"2017-08-31T23:19:02Z","abstract_excerpt":"Convolutional sparse representations are a form of sparse representation with a structured, translation invariant dictionary. Most convolutional dictionary learning algorithms to date operate in batch mode, requiring simultaneous access to all training images during the learning process, which results in very high memory usage and severely limits the training data that can be used. Very recently, however, a number of authors have considered the design of online convolutional dictionary learning algorithms that offer far better scaling of memory and computational cost with training set size tha"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.00106","kind":"arxiv","version":3},"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":"1709.00106","created_at":"2026-05-18T00:13:07.447269+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.00106v3","created_at":"2026-05-18T00:13:07.447269+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.00106","created_at":"2026-05-18T00:13:07.447269+00:00"},{"alias_kind":"pith_short_12","alias_value":"X2M2C5A3WRBE","created_at":"2026-05-18T12:31:53.515858+00:00"},{"alias_kind":"pith_short_16","alias_value":"X2M2C5A3WRBENBUK","created_at":"2026-05-18T12:31:53.515858+00:00"},{"alias_kind":"pith_short_8","alias_value":"X2M2C5A3","created_at":"2026-05-18T12:31:53.515858+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/X2M2C5A3WRBENBUKTDG5ANJRNF","json":"https://pith.science/pith/X2M2C5A3WRBENBUKTDG5ANJRNF.json","graph_json":"https://pith.science/api/pith-number/X2M2C5A3WRBENBUKTDG5ANJRNF/graph.json","events_json":"https://pith.science/api/pith-number/X2M2C5A3WRBENBUKTDG5ANJRNF/events.json","paper":"https://pith.science/paper/X2M2C5A3"},"agent_actions":{"view_html":"https://pith.science/pith/X2M2C5A3WRBENBUKTDG5ANJRNF","download_json":"https://pith.science/pith/X2M2C5A3WRBENBUKTDG5ANJRNF.json","view_paper":"https://pith.science/paper/X2M2C5A3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.00106&json=true","fetch_graph":"https://pith.science/api/pith-number/X2M2C5A3WRBENBUKTDG5ANJRNF/graph.json","fetch_events":"https://pith.science/api/pith-number/X2M2C5A3WRBENBUKTDG5ANJRNF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/X2M2C5A3WRBENBUKTDG5ANJRNF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/X2M2C5A3WRBENBUKTDG5ANJRNF/action/storage_attestation","attest_author":"https://pith.science/pith/X2M2C5A3WRBENBUKTDG5ANJRNF/action/author_attestation","sign_citation":"https://pith.science/pith/X2M2C5A3WRBENBUKTDG5ANJRNF/action/citation_signature","submit_replication":"https://pith.science/pith/X2M2C5A3WRBENBUKTDG5ANJRNF/action/replication_record"}},"created_at":"2026-05-18T00:13:07.447269+00:00","updated_at":"2026-05-18T00:13:07.447269+00:00"}