{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:7YVV63LX764WIHLW2BQDUMX7HN","short_pith_number":"pith:7YVV63LX","schema_version":"1.0","canonical_sha256":"fe2b5f6d77ffb9641d76d0603a32ff3b641b3a9cb77c69c7677ab18f09439844","source":{"kind":"arxiv","id":"1812.08119","version":1},"attestation_state":"computed","paper":{"title":"Adam Induces Implicit Weight Sparsity in Rectifier Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Akiyuki Tanizawa, Atsushi Yaguchi, Shuhei Nitta, Taiji Suzuki, Wataru Asano, Yukinobu Sakata","submitted_at":"2018-12-19T17:59:08Z","abstract_excerpt":"In recent years, deep neural networks (DNNs) have been applied to various machine leaning tasks, including image recognition, speech recognition, and machine translation. However, large DNN models are needed to achieve state-of-the-art performance, exceeding the capabilities of edge devices. Model reduction is thus needed for practical use. In this paper, we point out that deep learning automatically induces group sparsity of weights, in which all weights connected to an output channel (node) are zero, when training DNNs under the following three conditions: (1) rectified-linear-unit (ReLU) ac"},"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":"1812.08119","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-19T17:59:08Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"35652b3f0e250ca27792941f2cd70a15f38c23f145bfa9243d6b7f555412b236","abstract_canon_sha256":"90783ecd88ef5d9201a2fcf59ad1af9f567c4cd19d3e718577e8674ab97e8a4f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:57:54.795188Z","signature_b64":"Sz/80NGwvg47FlnY63uwNboDHxFAi2NhqxUUcPzykWa1CcXmEYrAcm3Nv7IF+Wff59GBiS4gV51orPyQRDbVDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fe2b5f6d77ffb9641d76d0603a32ff3b641b3a9cb77c69c7677ab18f09439844","last_reissued_at":"2026-05-17T23:57:54.794656Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:57:54.794656Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adam Induces Implicit Weight Sparsity in Rectifier Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Akiyuki Tanizawa, Atsushi Yaguchi, Shuhei Nitta, Taiji Suzuki, Wataru Asano, Yukinobu Sakata","submitted_at":"2018-12-19T17:59:08Z","abstract_excerpt":"In recent years, deep neural networks (DNNs) have been applied to various machine leaning tasks, including image recognition, speech recognition, and machine translation. However, large DNN models are needed to achieve state-of-the-art performance, exceeding the capabilities of edge devices. Model reduction is thus needed for practical use. In this paper, we point out that deep learning automatically induces group sparsity of weights, in which all weights connected to an output channel (node) are zero, when training DNNs under the following three conditions: (1) rectified-linear-unit (ReLU) ac"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.08119","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":"1812.08119","created_at":"2026-05-17T23:57:54.794740+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.08119v1","created_at":"2026-05-17T23:57:54.794740+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.08119","created_at":"2026-05-17T23:57:54.794740+00:00"},{"alias_kind":"pith_short_12","alias_value":"7YVV63LX764W","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_16","alias_value":"7YVV63LX764WIHLW","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_8","alias_value":"7YVV63LX","created_at":"2026-05-18T12:32:13.499390+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/7YVV63LX764WIHLW2BQDUMX7HN","json":"https://pith.science/pith/7YVV63LX764WIHLW2BQDUMX7HN.json","graph_json":"https://pith.science/api/pith-number/7YVV63LX764WIHLW2BQDUMX7HN/graph.json","events_json":"https://pith.science/api/pith-number/7YVV63LX764WIHLW2BQDUMX7HN/events.json","paper":"https://pith.science/paper/7YVV63LX"},"agent_actions":{"view_html":"https://pith.science/pith/7YVV63LX764WIHLW2BQDUMX7HN","download_json":"https://pith.science/pith/7YVV63LX764WIHLW2BQDUMX7HN.json","view_paper":"https://pith.science/paper/7YVV63LX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.08119&json=true","fetch_graph":"https://pith.science/api/pith-number/7YVV63LX764WIHLW2BQDUMX7HN/graph.json","fetch_events":"https://pith.science/api/pith-number/7YVV63LX764WIHLW2BQDUMX7HN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7YVV63LX764WIHLW2BQDUMX7HN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7YVV63LX764WIHLW2BQDUMX7HN/action/storage_attestation","attest_author":"https://pith.science/pith/7YVV63LX764WIHLW2BQDUMX7HN/action/author_attestation","sign_citation":"https://pith.science/pith/7YVV63LX764WIHLW2BQDUMX7HN/action/citation_signature","submit_replication":"https://pith.science/pith/7YVV63LX764WIHLW2BQDUMX7HN/action/replication_record"}},"created_at":"2026-05-17T23:57:54.794740+00:00","updated_at":"2026-05-17T23:57:54.794740+00:00"}