{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:SO3RKVMLP23ZR7XVI7FIGUQCCX","short_pith_number":"pith:SO3RKVML","schema_version":"1.0","canonical_sha256":"93b715558b7eb798fef547ca83520215c3b3a1af85190a0f9c79ec835f814a13","source":{"kind":"arxiv","id":"1703.00168","version":2},"attestation_state":"computed","paper":{"title":"Modular Representation of Layered Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Chihiro Watanabe, Kaoru Hiramatsu, Kunio Kashino","submitted_at":"2017-03-01T07:58:29Z","abstract_excerpt":"Layered neural networks have greatly improved the performance of various applications including image processing, speech recognition, natural language processing, and bioinformatics. However, it is still difficult to discover or interpret knowledge from the inference provided by a layered neural network, since its internal representation has many nonlinear and complex parameters embedded in hierarchical layers. Therefore, it becomes important to establish a new methodology by which layered neural networks can be understood.\n  In this paper, we propose a new method for extracting a global and s"},"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":"1703.00168","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-03-01T07:58:29Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"72629a23e02b38265e79a7b7d3fe4ff91f4610d505d82708490461031e7aeed9","abstract_canon_sha256":"7836f536f87c5855e61f5b4a858c2648cab776e905423a0feba425c8569fd18e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:33:43.795847Z","signature_b64":"m9gycYTHaQ6S3mZmfBuRtvAs1Auaj6DcA7kzbdN4MrwTlNCn/TZLIhrIkELyzPbPiu2CdLnUgDBcEP6bpNF9CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"93b715558b7eb798fef547ca83520215c3b3a1af85190a0f9c79ec835f814a13","last_reissued_at":"2026-05-18T00:33:43.795249Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:33:43.795249Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Modular Representation of Layered Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Chihiro Watanabe, Kaoru Hiramatsu, Kunio Kashino","submitted_at":"2017-03-01T07:58:29Z","abstract_excerpt":"Layered neural networks have greatly improved the performance of various applications including image processing, speech recognition, natural language processing, and bioinformatics. However, it is still difficult to discover or interpret knowledge from the inference provided by a layered neural network, since its internal representation has many nonlinear and complex parameters embedded in hierarchical layers. Therefore, it becomes important to establish a new methodology by which layered neural networks can be understood.\n  In this paper, we propose a new method for extracting a global and s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.00168","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":"1703.00168","created_at":"2026-05-18T00:33:43.795333+00:00"},{"alias_kind":"arxiv_version","alias_value":"1703.00168v2","created_at":"2026-05-18T00:33:43.795333+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.00168","created_at":"2026-05-18T00:33:43.795333+00:00"},{"alias_kind":"pith_short_12","alias_value":"SO3RKVMLP23Z","created_at":"2026-05-18T12:31:43.269735+00:00"},{"alias_kind":"pith_short_16","alias_value":"SO3RKVMLP23ZR7XV","created_at":"2026-05-18T12:31:43.269735+00:00"},{"alias_kind":"pith_short_8","alias_value":"SO3RKVML","created_at":"2026-05-18T12:31:43.269735+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/SO3RKVMLP23ZR7XVI7FIGUQCCX","json":"https://pith.science/pith/SO3RKVMLP23ZR7XVI7FIGUQCCX.json","graph_json":"https://pith.science/api/pith-number/SO3RKVMLP23ZR7XVI7FIGUQCCX/graph.json","events_json":"https://pith.science/api/pith-number/SO3RKVMLP23ZR7XVI7FIGUQCCX/events.json","paper":"https://pith.science/paper/SO3RKVML"},"agent_actions":{"view_html":"https://pith.science/pith/SO3RKVMLP23ZR7XVI7FIGUQCCX","download_json":"https://pith.science/pith/SO3RKVMLP23ZR7XVI7FIGUQCCX.json","view_paper":"https://pith.science/paper/SO3RKVML","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1703.00168&json=true","fetch_graph":"https://pith.science/api/pith-number/SO3RKVMLP23ZR7XVI7FIGUQCCX/graph.json","fetch_events":"https://pith.science/api/pith-number/SO3RKVMLP23ZR7XVI7FIGUQCCX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SO3RKVMLP23ZR7XVI7FIGUQCCX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SO3RKVMLP23ZR7XVI7FIGUQCCX/action/storage_attestation","attest_author":"https://pith.science/pith/SO3RKVMLP23ZR7XVI7FIGUQCCX/action/author_attestation","sign_citation":"https://pith.science/pith/SO3RKVMLP23ZR7XVI7FIGUQCCX/action/citation_signature","submit_replication":"https://pith.science/pith/SO3RKVMLP23ZR7XVI7FIGUQCCX/action/replication_record"}},"created_at":"2026-05-18T00:33:43.795333+00:00","updated_at":"2026-05-18T00:33:43.795333+00:00"}