{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:ZM4JLG7LXFH5TAIONSDTACJ4AU","short_pith_number":"pith:ZM4JLG7L","schema_version":"1.0","canonical_sha256":"cb38959bebb94fd9810e6c8730093c0505c833a75283eb8a6a88ab59920138fa","source":{"kind":"arxiv","id":"1810.09284","version":3},"attestation_state":"computed","paper":{"title":"Gradient target propagation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jonas Maziero, Tiago de Souza Farias","submitted_at":"2018-10-19T15:56:00Z","abstract_excerpt":"We report a learning rule for neural networks that computes how much each neuron should contribute to minimize a giving cost function via the estimation of its target value. By theoretical analysis, we show that this learning rule contains backpropagation, Hebian learning, and additional terms. We also give a general technique for weights initialization. Our results are at least as good as those obtained with backpropagation. The neural networks are trained and tested in three problems: MNIST, MNIST-Fashion, and CIFAR-10 datasets. The associated code is available at https://github.com/tiago939"},"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":"1810.09284","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-19T15:56:00Z","cross_cats_sorted":[],"title_canon_sha256":"003e90bef8f88e0d393b0edc9e6d33579e6a7206373cba720997c15b0846ec56","abstract_canon_sha256":"c9cc1d68638067a4355c7ea088b4ab4e9eb7128dd565dc2c8721e29b24c80d65"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:46.910252Z","signature_b64":"gEWsH0kOoK3W/5IqBRIVVlxoRVN6S5dk/3Ba9IBRNOPqCohc+U/vfYyo5cLkxW6UL5WNrPGZB0lCP09YdNgRCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cb38959bebb94fd9810e6c8730093c0505c833a75283eb8a6a88ab59920138fa","last_reissued_at":"2026-05-18T00:01:46.909750Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:46.909750Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Gradient target propagation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jonas Maziero, Tiago de Souza Farias","submitted_at":"2018-10-19T15:56:00Z","abstract_excerpt":"We report a learning rule for neural networks that computes how much each neuron should contribute to minimize a giving cost function via the estimation of its target value. By theoretical analysis, we show that this learning rule contains backpropagation, Hebian learning, and additional terms. We also give a general technique for weights initialization. Our results are at least as good as those obtained with backpropagation. The neural networks are trained and tested in three problems: MNIST, MNIST-Fashion, and CIFAR-10 datasets. The associated code is available at https://github.com/tiago939"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.09284","kind":"arxiv","version":3},"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":"1810.09284","created_at":"2026-05-18T00:01:46.909839+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.09284v3","created_at":"2026-05-18T00:01:46.909839+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.09284","created_at":"2026-05-18T00:01:46.909839+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZM4JLG7LXFH5","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZM4JLG7LXFH5TAIO","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZM4JLG7L","created_at":"2026-05-18T12:33:07.085635+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/ZM4JLG7LXFH5TAIONSDTACJ4AU","json":"https://pith.science/pith/ZM4JLG7LXFH5TAIONSDTACJ4AU.json","graph_json":"https://pith.science/api/pith-number/ZM4JLG7LXFH5TAIONSDTACJ4AU/graph.json","events_json":"https://pith.science/api/pith-number/ZM4JLG7LXFH5TAIONSDTACJ4AU/events.json","paper":"https://pith.science/paper/ZM4JLG7L"},"agent_actions":{"view_html":"https://pith.science/pith/ZM4JLG7LXFH5TAIONSDTACJ4AU","download_json":"https://pith.science/pith/ZM4JLG7LXFH5TAIONSDTACJ4AU.json","view_paper":"https://pith.science/paper/ZM4JLG7L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.09284&json=true","fetch_graph":"https://pith.science/api/pith-number/ZM4JLG7LXFH5TAIONSDTACJ4AU/graph.json","fetch_events":"https://pith.science/api/pith-number/ZM4JLG7LXFH5TAIONSDTACJ4AU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZM4JLG7LXFH5TAIONSDTACJ4AU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZM4JLG7LXFH5TAIONSDTACJ4AU/action/storage_attestation","attest_author":"https://pith.science/pith/ZM4JLG7LXFH5TAIONSDTACJ4AU/action/author_attestation","sign_citation":"https://pith.science/pith/ZM4JLG7LXFH5TAIONSDTACJ4AU/action/citation_signature","submit_replication":"https://pith.science/pith/ZM4JLG7LXFH5TAIONSDTACJ4AU/action/replication_record"}},"created_at":"2026-05-18T00:01:46.909839+00:00","updated_at":"2026-05-18T00:01:46.909839+00:00"}