{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:ROPSFXTYPB6ZTL6XB2PXNQ4MJF","short_pith_number":"pith:ROPSFXTY","schema_version":"1.0","canonical_sha256":"8b9f22de78787d99afd70e9f76c38c496794aad5200463816592ba53cb51ed0a","source":{"kind":"arxiv","id":"1708.08557","version":2},"attestation_state":"computed","paper":{"title":"A parameterized activation function for learning fuzzy logic operations in deep neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Luke B. Godfrey, Michael S. Gashler","submitted_at":"2017-08-28T23:08:21Z","abstract_excerpt":"We present a deep learning architecture for learning fuzzy logic expressions. Our model uses an innovative, parameterized, differentiable activation function that can learn a number of logical operations by gradient descent. This activation function allows a neural network to determine the relationships between its input variables and provides insight into the logical significance of learned network parameters. We provide a theoretical basis for this parameterization and demonstrate its effectiveness and utility by successfully applying our model to five classification problems from the UCI Ma"},"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":"1708.08557","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-08-28T23:08:21Z","cross_cats_sorted":[],"title_canon_sha256":"8364878330721a97c455a9871c51f26209c8422a6238987009be59e298105d8c","abstract_canon_sha256":"27dcf627782c41c219d8cae336a1a7f76a176ff043613341cd57d16417810803"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:35:30.465651Z","signature_b64":"vDfW+gZqoB7HA/PSULF5oGAl6SfEYfCZCoL7x6pzR580drbF8Th+cYl1xPTEgB+IMaL4t16fU0L0ekguDfyvCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8b9f22de78787d99afd70e9f76c38c496794aad5200463816592ba53cb51ed0a","last_reissued_at":"2026-05-18T00:35:30.465130Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:35:30.465130Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A parameterized activation function for learning fuzzy logic operations in deep neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Luke B. Godfrey, Michael S. Gashler","submitted_at":"2017-08-28T23:08:21Z","abstract_excerpt":"We present a deep learning architecture for learning fuzzy logic expressions. Our model uses an innovative, parameterized, differentiable activation function that can learn a number of logical operations by gradient descent. This activation function allows a neural network to determine the relationships between its input variables and provides insight into the logical significance of learned network parameters. We provide a theoretical basis for this parameterization and demonstrate its effectiveness and utility by successfully applying our model to five classification problems from the UCI Ma"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.08557","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":"1708.08557","created_at":"2026-05-18T00:35:30.465205+00:00"},{"alias_kind":"arxiv_version","alias_value":"1708.08557v2","created_at":"2026-05-18T00:35:30.465205+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.08557","created_at":"2026-05-18T00:35:30.465205+00:00"},{"alias_kind":"pith_short_12","alias_value":"ROPSFXTYPB6Z","created_at":"2026-05-18T12:31:39.905425+00:00"},{"alias_kind":"pith_short_16","alias_value":"ROPSFXTYPB6ZTL6X","created_at":"2026-05-18T12:31:39.905425+00:00"},{"alias_kind":"pith_short_8","alias_value":"ROPSFXTY","created_at":"2026-05-18T12:31:39.905425+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/ROPSFXTYPB6ZTL6XB2PXNQ4MJF","json":"https://pith.science/pith/ROPSFXTYPB6ZTL6XB2PXNQ4MJF.json","graph_json":"https://pith.science/api/pith-number/ROPSFXTYPB6ZTL6XB2PXNQ4MJF/graph.json","events_json":"https://pith.science/api/pith-number/ROPSFXTYPB6ZTL6XB2PXNQ4MJF/events.json","paper":"https://pith.science/paper/ROPSFXTY"},"agent_actions":{"view_html":"https://pith.science/pith/ROPSFXTYPB6ZTL6XB2PXNQ4MJF","download_json":"https://pith.science/pith/ROPSFXTYPB6ZTL6XB2PXNQ4MJF.json","view_paper":"https://pith.science/paper/ROPSFXTY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1708.08557&json=true","fetch_graph":"https://pith.science/api/pith-number/ROPSFXTYPB6ZTL6XB2PXNQ4MJF/graph.json","fetch_events":"https://pith.science/api/pith-number/ROPSFXTYPB6ZTL6XB2PXNQ4MJF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ROPSFXTYPB6ZTL6XB2PXNQ4MJF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ROPSFXTYPB6ZTL6XB2PXNQ4MJF/action/storage_attestation","attest_author":"https://pith.science/pith/ROPSFXTYPB6ZTL6XB2PXNQ4MJF/action/author_attestation","sign_citation":"https://pith.science/pith/ROPSFXTYPB6ZTL6XB2PXNQ4MJF/action/citation_signature","submit_replication":"https://pith.science/pith/ROPSFXTYPB6ZTL6XB2PXNQ4MJF/action/replication_record"}},"created_at":"2026-05-18T00:35:30.465205+00:00","updated_at":"2026-05-18T00:35:30.465205+00:00"}