{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:7RTYT42AQR4MI2VKSQLOGNYO2L","short_pith_number":"pith:7RTYT42A","schema_version":"1.0","canonical_sha256":"fc6789f3408478c46aaa9416e3370ed2c10c3fb75b437024a0588c5fa34845ca","source":{"kind":"arxiv","id":"1701.08939","version":1},"attestation_state":"computed","paper":{"title":"Deep Submodular Functions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jeffrey Bilmes, Wenruo Bai","submitted_at":"2017-01-31T08:06:33Z","abstract_excerpt":"We start with an overview of a class of submodular functions called SCMMs (sums of concave composed with non-negative modular functions plus a final arbitrary modular). We then define a new class of submodular functions we call {\\em deep submodular functions} or DSFs. We show that DSFs are a flexible parametric family of submodular functions that share many of the properties and advantages of deep neural networks (DNNs). DSFs can be motivated by considering a hierarchy of descriptive concepts over ground elements and where one wishes to allow submodular interaction throughout this hierarchy. R"},"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":"1701.08939","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-01-31T08:06:33Z","cross_cats_sorted":[],"title_canon_sha256":"fa8314b33ca54c6b109b96a62b40592de866a9062b625214012c69c8510e154f","abstract_canon_sha256":"ea9c5b3af640352443f11b51831ee0b359ae86583a5b2bb3d05e8297ffe7da95"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:51:38.850995Z","signature_b64":"zdeMmnDJYXxARIpmSW8+7Fq1i1xFtjlFrK0UzWTsaXiKvMvw5JAScKEmFAo8DHU7gPIur6osm316MN4m3emXDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fc6789f3408478c46aaa9416e3370ed2c10c3fb75b437024a0588c5fa34845ca","last_reissued_at":"2026-05-18T00:51:38.850628Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:51:38.850628Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Submodular Functions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jeffrey Bilmes, Wenruo Bai","submitted_at":"2017-01-31T08:06:33Z","abstract_excerpt":"We start with an overview of a class of submodular functions called SCMMs (sums of concave composed with non-negative modular functions plus a final arbitrary modular). We then define a new class of submodular functions we call {\\em deep submodular functions} or DSFs. We show that DSFs are a flexible parametric family of submodular functions that share many of the properties and advantages of deep neural networks (DNNs). DSFs can be motivated by considering a hierarchy of descriptive concepts over ground elements and where one wishes to allow submodular interaction throughout this hierarchy. R"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.08939","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":""},"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":"1701.08939","created_at":"2026-05-18T00:51:38.850683+00:00"},{"alias_kind":"arxiv_version","alias_value":"1701.08939v1","created_at":"2026-05-18T00:51:38.850683+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.08939","created_at":"2026-05-18T00:51:38.850683+00:00"},{"alias_kind":"pith_short_12","alias_value":"7RTYT42AQR4M","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_16","alias_value":"7RTYT42AQR4MI2VK","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_8","alias_value":"7RTYT42A","created_at":"2026-05-18T12:31:05.417338+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/7RTYT42AQR4MI2VKSQLOGNYO2L","json":"https://pith.science/pith/7RTYT42AQR4MI2VKSQLOGNYO2L.json","graph_json":"https://pith.science/api/pith-number/7RTYT42AQR4MI2VKSQLOGNYO2L/graph.json","events_json":"https://pith.science/api/pith-number/7RTYT42AQR4MI2VKSQLOGNYO2L/events.json","paper":"https://pith.science/paper/7RTYT42A"},"agent_actions":{"view_html":"https://pith.science/pith/7RTYT42AQR4MI2VKSQLOGNYO2L","download_json":"https://pith.science/pith/7RTYT42AQR4MI2VKSQLOGNYO2L.json","view_paper":"https://pith.science/paper/7RTYT42A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1701.08939&json=true","fetch_graph":"https://pith.science/api/pith-number/7RTYT42AQR4MI2VKSQLOGNYO2L/graph.json","fetch_events":"https://pith.science/api/pith-number/7RTYT42AQR4MI2VKSQLOGNYO2L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7RTYT42AQR4MI2VKSQLOGNYO2L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7RTYT42AQR4MI2VKSQLOGNYO2L/action/storage_attestation","attest_author":"https://pith.science/pith/7RTYT42AQR4MI2VKSQLOGNYO2L/action/author_attestation","sign_citation":"https://pith.science/pith/7RTYT42AQR4MI2VKSQLOGNYO2L/action/citation_signature","submit_replication":"https://pith.science/pith/7RTYT42AQR4MI2VKSQLOGNYO2L/action/replication_record"}},"created_at":"2026-05-18T00:51:38.850683+00:00","updated_at":"2026-05-18T00:51:38.850683+00:00"}