{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:XHWRUNVNZYYONNPMLE5XSVE7RZ","short_pith_number":"pith:XHWRUNVN","schema_version":"1.0","canonical_sha256":"b9ed1a36adce30e6b5ec593b79549f8e707a6af67e3cb41920656e3787295740","source":{"kind":"arxiv","id":"1306.0152","version":1},"attestation_state":"computed","paper":{"title":"An Analysis of the Connections Between Layers of Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aysegul Dundar, Eugenio Culurciello, Jonghoon Jin, Jordan Bates","submitted_at":"2013-06-01T21:37:25Z","abstract_excerpt":"We present an analysis of different techniques for selecting the connection be- tween layers of deep neural networks. Traditional deep neural networks use ran- dom connection tables between layers to keep the number of connections small and tune to different image features. This kind of connection performs adequately in supervised deep networks because their values are refined during the training. On the other hand, in unsupervised learning, one cannot rely on back-propagation techniques to learn the connections between layers. In this work, we tested four different techniques for connecting t"},"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":"1306.0152","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2013-06-01T21:37:25Z","cross_cats_sorted":[],"title_canon_sha256":"c34cc236f22dc9a0ab09439e83d69c19691d0cd54320b51f708d02088eb20e4f","abstract_canon_sha256":"d8c04963135516e820a877703cddb537e32f292e65bdbdc845c1d4194efe6597"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:21:56.022516Z","signature_b64":"y8NW7AglTbtXPC1QeQErKg3A5Ex3GC7CNlg1s8hK3A3ElHEcITgpCN5zONOKkH28N67Gc69BOUc67+iLgFjjBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b9ed1a36adce30e6b5ec593b79549f8e707a6af67e3cb41920656e3787295740","last_reissued_at":"2026-05-18T03:21:56.022151Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:21:56.022151Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Analysis of the Connections Between Layers of Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aysegul Dundar, Eugenio Culurciello, Jonghoon Jin, Jordan Bates","submitted_at":"2013-06-01T21:37:25Z","abstract_excerpt":"We present an analysis of different techniques for selecting the connection be- tween layers of deep neural networks. Traditional deep neural networks use ran- dom connection tables between layers to keep the number of connections small and tune to different image features. This kind of connection performs adequately in supervised deep networks because their values are refined during the training. On the other hand, in unsupervised learning, one cannot rely on back-propagation techniques to learn the connections between layers. In this work, we tested four different techniques for connecting t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1306.0152","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":"1306.0152","created_at":"2026-05-18T03:21:56.022201+00:00"},{"alias_kind":"arxiv_version","alias_value":"1306.0152v1","created_at":"2026-05-18T03:21:56.022201+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1306.0152","created_at":"2026-05-18T03:21:56.022201+00:00"},{"alias_kind":"pith_short_12","alias_value":"XHWRUNVNZYYO","created_at":"2026-05-18T12:28:06.772260+00:00"},{"alias_kind":"pith_short_16","alias_value":"XHWRUNVNZYYONNPM","created_at":"2026-05-18T12:28:06.772260+00:00"},{"alias_kind":"pith_short_8","alias_value":"XHWRUNVN","created_at":"2026-05-18T12:28:06.772260+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/XHWRUNVNZYYONNPMLE5XSVE7RZ","json":"https://pith.science/pith/XHWRUNVNZYYONNPMLE5XSVE7RZ.json","graph_json":"https://pith.science/api/pith-number/XHWRUNVNZYYONNPMLE5XSVE7RZ/graph.json","events_json":"https://pith.science/api/pith-number/XHWRUNVNZYYONNPMLE5XSVE7RZ/events.json","paper":"https://pith.science/paper/XHWRUNVN"},"agent_actions":{"view_html":"https://pith.science/pith/XHWRUNVNZYYONNPMLE5XSVE7RZ","download_json":"https://pith.science/pith/XHWRUNVNZYYONNPMLE5XSVE7RZ.json","view_paper":"https://pith.science/paper/XHWRUNVN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1306.0152&json=true","fetch_graph":"https://pith.science/api/pith-number/XHWRUNVNZYYONNPMLE5XSVE7RZ/graph.json","fetch_events":"https://pith.science/api/pith-number/XHWRUNVNZYYONNPMLE5XSVE7RZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XHWRUNVNZYYONNPMLE5XSVE7RZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XHWRUNVNZYYONNPMLE5XSVE7RZ/action/storage_attestation","attest_author":"https://pith.science/pith/XHWRUNVNZYYONNPMLE5XSVE7RZ/action/author_attestation","sign_citation":"https://pith.science/pith/XHWRUNVNZYYONNPMLE5XSVE7RZ/action/citation_signature","submit_replication":"https://pith.science/pith/XHWRUNVNZYYONNPMLE5XSVE7RZ/action/replication_record"}},"created_at":"2026-05-18T03:21:56.022201+00:00","updated_at":"2026-05-18T03:21:56.022201+00:00"}