{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:L6IGW2CCQTA6H3ZNR5I5KMN2BK","short_pith_number":"pith:L6IGW2CC","schema_version":"1.0","canonical_sha256":"5f906b684284c1e3ef2d8f51d531ba0ab90fbd56a866b009b9bd53b1a6807f21","source":{"kind":"arxiv","id":"1707.09725","version":1},"attestation_state":"computed","paper":{"title":"Analysis and Optimization of Convolutional Neural Network Architectures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Martin Thoma","submitted_at":"2017-07-31T05:35:12Z","abstract_excerpt":"Convolutional Neural Networks (CNNs) dominate various computer vision tasks since Alex Krizhevsky showed that they can be trained effectively and reduced the top-5 error from 26.2 % to 15.3 % on the ImageNet large scale visual recognition challenge. Many aspects of CNNs are examined in various publications, but literature about the analysis and construction of neural network architectures is rare. This work is one step to close this gap. A comprehensive overview over existing techniques for CNN analysis and topology construction is provided. A novel way to visualize classification errors with "},"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":"1707.09725","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-07-31T05:35:12Z","cross_cats_sorted":[],"title_canon_sha256":"f6822b8ff59f056b623f3fec55ea0a193d75bca497ec5cb41ada6122d7cdcdf7","abstract_canon_sha256":"75dd413eca50332b5823f014062a4e88a18d487178506b392d0c927e34bd3af8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:39:08.465136Z","signature_b64":"DdF9KRbRiOjMxrALqtlvTnKkrtxbnB7rxCdDZIOdmBOT0IcGZiKOHtnbDjy9Ew6J652wwWDjkE4iEEi49O0uAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5f906b684284c1e3ef2d8f51d531ba0ab90fbd56a866b009b9bd53b1a6807f21","last_reissued_at":"2026-05-18T00:39:08.464562Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:39:08.464562Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Analysis and Optimization of Convolutional Neural Network Architectures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Martin Thoma","submitted_at":"2017-07-31T05:35:12Z","abstract_excerpt":"Convolutional Neural Networks (CNNs) dominate various computer vision tasks since Alex Krizhevsky showed that they can be trained effectively and reduced the top-5 error from 26.2 % to 15.3 % on the ImageNet large scale visual recognition challenge. Many aspects of CNNs are examined in various publications, but literature about the analysis and construction of neural network architectures is rare. This work is one step to close this gap. A comprehensive overview over existing techniques for CNN analysis and topology construction is provided. A novel way to visualize classification errors with "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.09725","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":"1707.09725","created_at":"2026-05-18T00:39:08.464649+00:00"},{"alias_kind":"arxiv_version","alias_value":"1707.09725v1","created_at":"2026-05-18T00:39:08.464649+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.09725","created_at":"2026-05-18T00:39:08.464649+00:00"},{"alias_kind":"pith_short_12","alias_value":"L6IGW2CCQTA6","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_16","alias_value":"L6IGW2CCQTA6H3ZN","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_8","alias_value":"L6IGW2CC","created_at":"2026-05-18T12:31:28.150371+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/L6IGW2CCQTA6H3ZNR5I5KMN2BK","json":"https://pith.science/pith/L6IGW2CCQTA6H3ZNR5I5KMN2BK.json","graph_json":"https://pith.science/api/pith-number/L6IGW2CCQTA6H3ZNR5I5KMN2BK/graph.json","events_json":"https://pith.science/api/pith-number/L6IGW2CCQTA6H3ZNR5I5KMN2BK/events.json","paper":"https://pith.science/paper/L6IGW2CC"},"agent_actions":{"view_html":"https://pith.science/pith/L6IGW2CCQTA6H3ZNR5I5KMN2BK","download_json":"https://pith.science/pith/L6IGW2CCQTA6H3ZNR5I5KMN2BK.json","view_paper":"https://pith.science/paper/L6IGW2CC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1707.09725&json=true","fetch_graph":"https://pith.science/api/pith-number/L6IGW2CCQTA6H3ZNR5I5KMN2BK/graph.json","fetch_events":"https://pith.science/api/pith-number/L6IGW2CCQTA6H3ZNR5I5KMN2BK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L6IGW2CCQTA6H3ZNR5I5KMN2BK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L6IGW2CCQTA6H3ZNR5I5KMN2BK/action/storage_attestation","attest_author":"https://pith.science/pith/L6IGW2CCQTA6H3ZNR5I5KMN2BK/action/author_attestation","sign_citation":"https://pith.science/pith/L6IGW2CCQTA6H3ZNR5I5KMN2BK/action/citation_signature","submit_replication":"https://pith.science/pith/L6IGW2CCQTA6H3ZNR5I5KMN2BK/action/replication_record"}},"created_at":"2026-05-18T00:39:08.464649+00:00","updated_at":"2026-05-18T00:39:08.464649+00:00"}