{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:C623CCBGL5O7CNIW22JNKPXLOE","short_pith_number":"pith:C623CCBG","schema_version":"1.0","canonical_sha256":"17b5b108265f5df13516d692d53eeb71295ef485f0ec1f117804847229779f3b","source":{"kind":"arxiv","id":"2101.11883","version":1},"attestation_state":"computed","paper":{"title":"Evolutionary Neural Architecture Search Supporting Approximate Multipliers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Lukas Sekanina, Michal Pinos, Vojtech Mrazek","submitted_at":"2021-01-28T09:26:03Z","abstract_excerpt":"There is a growing interest in automated neural architecture search (NAS) methods. They are employed to routinely deliver high-quality neural network architectures for various challenging data sets and reduce the designer's effort. The NAS methods utilizing multi-objective evolutionary algorithms are especially useful when the objective is not only to minimize the network error but also to minimize the number of parameters (weights) or power consumption of the inference phase. We propose a multi-objective NAS method based on Cartesian genetic programming for evolving convolutional neural netwo"},"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":"2101.11883","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2021-01-28T09:26:03Z","cross_cats_sorted":[],"title_canon_sha256":"17deb01ebe2b836ebc8e1db8db2e94beb237bef8c612916621ed9dbbade5a33e","abstract_canon_sha256":"732d0f30c2cd1ecd811699290a058922b235a2a0f79d7273738785f1b29571b5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:34:44.825262Z","signature_b64":"AJAzn5bj8yucddvgsBLSrSWHr/Wd63uQweRP2vSq1nfjk0RMup89ppdFv4Se3LXW/wgnuGktJDsqa3pYo2ZDDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"17b5b108265f5df13516d692d53eeb71295ef485f0ec1f117804847229779f3b","last_reissued_at":"2026-07-05T04:34:44.824767Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:34:44.824767Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evolutionary Neural Architecture Search Supporting Approximate Multipliers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Lukas Sekanina, Michal Pinos, Vojtech Mrazek","submitted_at":"2021-01-28T09:26:03Z","abstract_excerpt":"There is a growing interest in automated neural architecture search (NAS) methods. They are employed to routinely deliver high-quality neural network architectures for various challenging data sets and reduce the designer's effort. The NAS methods utilizing multi-objective evolutionary algorithms are especially useful when the objective is not only to minimize the network error but also to minimize the number of parameters (weights) or power consumption of the inference phase. We propose a multi-objective NAS method based on Cartesian genetic programming for evolving convolutional neural netwo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.11883","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2101.11883/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2101.11883","created_at":"2026-07-05T04:34:44.824827+00:00"},{"alias_kind":"arxiv_version","alias_value":"2101.11883v1","created_at":"2026-07-05T04:34:44.824827+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.11883","created_at":"2026-07-05T04:34:44.824827+00:00"},{"alias_kind":"pith_short_12","alias_value":"C623CCBGL5O7","created_at":"2026-07-05T04:34:44.824827+00:00"},{"alias_kind":"pith_short_16","alias_value":"C623CCBGL5O7CNIW","created_at":"2026-07-05T04:34:44.824827+00:00"},{"alias_kind":"pith_short_8","alias_value":"C623CCBG","created_at":"2026-07-05T04:34:44.824827+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/C623CCBGL5O7CNIW22JNKPXLOE","json":"https://pith.science/pith/C623CCBGL5O7CNIW22JNKPXLOE.json","graph_json":"https://pith.science/api/pith-number/C623CCBGL5O7CNIW22JNKPXLOE/graph.json","events_json":"https://pith.science/api/pith-number/C623CCBGL5O7CNIW22JNKPXLOE/events.json","paper":"https://pith.science/paper/C623CCBG"},"agent_actions":{"view_html":"https://pith.science/pith/C623CCBGL5O7CNIW22JNKPXLOE","download_json":"https://pith.science/pith/C623CCBGL5O7CNIW22JNKPXLOE.json","view_paper":"https://pith.science/paper/C623CCBG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2101.11883&json=true","fetch_graph":"https://pith.science/api/pith-number/C623CCBGL5O7CNIW22JNKPXLOE/graph.json","fetch_events":"https://pith.science/api/pith-number/C623CCBGL5O7CNIW22JNKPXLOE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C623CCBGL5O7CNIW22JNKPXLOE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C623CCBGL5O7CNIW22JNKPXLOE/action/storage_attestation","attest_author":"https://pith.science/pith/C623CCBGL5O7CNIW22JNKPXLOE/action/author_attestation","sign_citation":"https://pith.science/pith/C623CCBGL5O7CNIW22JNKPXLOE/action/citation_signature","submit_replication":"https://pith.science/pith/C623CCBGL5O7CNIW22JNKPXLOE/action/replication_record"}},"created_at":"2026-07-05T04:34:44.824827+00:00","updated_at":"2026-07-05T04:34:44.824827+00:00"}