{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:EUHF6N5AB5ZJGQGKHPIU4ZG2RB","short_pith_number":"pith:EUHF6N5A","schema_version":"1.0","canonical_sha256":"250e5f37a00f729340ca3bd14e64da88706ef27e488226ef0b0cfede15e6c09d","source":{"kind":"arxiv","id":"1611.05552","version":5},"attestation_state":"computed","paper":{"title":"DelugeNets: Deep Networks with Efficient and Flexible Cross-layer Information Inflows","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"cs.CV","authors_text":"Gang Wang, Jason Kuen, Xiangfei Kong, Yap-Peng Tan","submitted_at":"2016-11-17T03:45:48Z","abstract_excerpt":"Deluge Networks (DelugeNets) are deep neural networks which efficiently facilitate massive cross-layer information inflows from preceding layers to succeeding layers. The connections between layers in DelugeNets are established through cross-layer depthwise convolutional layers with learnable filters, acting as a flexible yet efficient selection mechanism. DelugeNets can propagate information across many layers with greater flexibility and utilize network parameters more effectively compared to ResNets, whilst being more efficient than DenseNets. Remarkably, a DelugeNet model with just model c"},"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":"1611.05552","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-17T03:45:48Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"b5da4fe21eeaab345149333dc2eadfc9222ad5676940e30cfef8a64f3c374116","abstract_canon_sha256":"5a2abf52f2ac6c0e87e7ac61e41167a4c7cca22f113403351eb59358d456f8bc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:36:51.459139Z","signature_b64":"ZvfABVFnSsStxIW+dP19VpCIeUaiuvwfX/Its3GAgU4uiQDYGIkuB9it4GOAvd9cIe8HpotDlV5nqdgvayytBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"250e5f37a00f729340ca3bd14e64da88706ef27e488226ef0b0cfede15e6c09d","last_reissued_at":"2026-05-18T00:36:51.458636Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:36:51.458636Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DelugeNets: Deep Networks with Efficient and Flexible Cross-layer Information Inflows","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"cs.CV","authors_text":"Gang Wang, Jason Kuen, Xiangfei Kong, Yap-Peng Tan","submitted_at":"2016-11-17T03:45:48Z","abstract_excerpt":"Deluge Networks (DelugeNets) are deep neural networks which efficiently facilitate massive cross-layer information inflows from preceding layers to succeeding layers. The connections between layers in DelugeNets are established through cross-layer depthwise convolutional layers with learnable filters, acting as a flexible yet efficient selection mechanism. DelugeNets can propagate information across many layers with greater flexibility and utilize network parameters more effectively compared to ResNets, whilst being more efficient than DenseNets. Remarkably, a DelugeNet model with just model c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.05552","kind":"arxiv","version":5},"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":"1611.05552","created_at":"2026-05-18T00:36:51.458721+00:00"},{"alias_kind":"arxiv_version","alias_value":"1611.05552v5","created_at":"2026-05-18T00:36:51.458721+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.05552","created_at":"2026-05-18T00:36:51.458721+00:00"},{"alias_kind":"pith_short_12","alias_value":"EUHF6N5AB5ZJ","created_at":"2026-05-18T12:30:15.759754+00:00"},{"alias_kind":"pith_short_16","alias_value":"EUHF6N5AB5ZJGQGK","created_at":"2026-05-18T12:30:15.759754+00:00"},{"alias_kind":"pith_short_8","alias_value":"EUHF6N5A","created_at":"2026-05-18T12:30:15.759754+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/EUHF6N5AB5ZJGQGKHPIU4ZG2RB","json":"https://pith.science/pith/EUHF6N5AB5ZJGQGKHPIU4ZG2RB.json","graph_json":"https://pith.science/api/pith-number/EUHF6N5AB5ZJGQGKHPIU4ZG2RB/graph.json","events_json":"https://pith.science/api/pith-number/EUHF6N5AB5ZJGQGKHPIU4ZG2RB/events.json","paper":"https://pith.science/paper/EUHF6N5A"},"agent_actions":{"view_html":"https://pith.science/pith/EUHF6N5AB5ZJGQGKHPIU4ZG2RB","download_json":"https://pith.science/pith/EUHF6N5AB5ZJGQGKHPIU4ZG2RB.json","view_paper":"https://pith.science/paper/EUHF6N5A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1611.05552&json=true","fetch_graph":"https://pith.science/api/pith-number/EUHF6N5AB5ZJGQGKHPIU4ZG2RB/graph.json","fetch_events":"https://pith.science/api/pith-number/EUHF6N5AB5ZJGQGKHPIU4ZG2RB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EUHF6N5AB5ZJGQGKHPIU4ZG2RB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EUHF6N5AB5ZJGQGKHPIU4ZG2RB/action/storage_attestation","attest_author":"https://pith.science/pith/EUHF6N5AB5ZJGQGKHPIU4ZG2RB/action/author_attestation","sign_citation":"https://pith.science/pith/EUHF6N5AB5ZJGQGKHPIU4ZG2RB/action/citation_signature","submit_replication":"https://pith.science/pith/EUHF6N5AB5ZJGQGKHPIU4ZG2RB/action/replication_record"}},"created_at":"2026-05-18T00:36:51.458721+00:00","updated_at":"2026-05-18T00:36:51.458721+00:00"}