{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:UVZ32IURSJ7NOGUEM4WM5FEJWA","short_pith_number":"pith:UVZ32IUR","schema_version":"1.0","canonical_sha256":"a573bd2291927ed71a84672cce9489b00a9d590734d71cdfc6214bb0a0775429","source":{"kind":"arxiv","id":"1711.01761","version":1},"attestation_state":"computed","paper":{"title":"AdaBatch: Efficient Gradient Aggregation Rules for Sequential and Parallel Stochastic Gradient Methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Alexandre D\\'efossez (FAIR), Francis Bach (SIERRA)","submitted_at":"2017-11-06T07:52:34Z","abstract_excerpt":"We study a new aggregation operator for gradients coming from a mini-batch for stochastic gradient (SG) methods that allows a significant speed-up in the case of sparse optimization problems. We call this method AdaBatch and it only requires a few lines of code change compared to regular mini-batch SGD algorithms. We provide a theoretical insight to understand how this new class of algorithms is performing and show that it is equivalent to an implicit per-coordinate rescaling of the gradients, similarly to what Adagrad methods can do. In theory and in practice, this new aggregation allows to k"},"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":"1711.01761","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-06T07:52:34Z","cross_cats_sorted":["math.OC","stat.ML"],"title_canon_sha256":"2c41aaea28117557a765b4eed1b7dbeaf168b03a3b617c6ffd296ba1eb4b8792","abstract_canon_sha256":"06ffd02a67e7fa3be8294d5805af83a7468a11bca066d710ea9da3848f116c79"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:31:16.919241Z","signature_b64":"5SKAktoPfg4o6vPh+Iicmb7JMyjJQd6Gj2utQ5pESfLlBw944YLzh5F5J7aCrVqAvpbStNdDe9YwgLUppLUNCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a573bd2291927ed71a84672cce9489b00a9d590734d71cdfc6214bb0a0775429","last_reissued_at":"2026-05-18T00:31:16.918851Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:31:16.918851Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AdaBatch: Efficient Gradient Aggregation Rules for Sequential and Parallel Stochastic Gradient Methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Alexandre D\\'efossez (FAIR), Francis Bach (SIERRA)","submitted_at":"2017-11-06T07:52:34Z","abstract_excerpt":"We study a new aggregation operator for gradients coming from a mini-batch for stochastic gradient (SG) methods that allows a significant speed-up in the case of sparse optimization problems. We call this method AdaBatch and it only requires a few lines of code change compared to regular mini-batch SGD algorithms. We provide a theoretical insight to understand how this new class of algorithms is performing and show that it is equivalent to an implicit per-coordinate rescaling of the gradients, similarly to what Adagrad methods can do. In theory and in practice, this new aggregation allows to k"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.01761","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":"1711.01761","created_at":"2026-05-18T00:31:16.918914+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.01761v1","created_at":"2026-05-18T00:31:16.918914+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.01761","created_at":"2026-05-18T00:31:16.918914+00:00"},{"alias_kind":"pith_short_12","alias_value":"UVZ32IURSJ7N","created_at":"2026-05-18T12:31:49.984773+00:00"},{"alias_kind":"pith_short_16","alias_value":"UVZ32IURSJ7NOGUE","created_at":"2026-05-18T12:31:49.984773+00:00"},{"alias_kind":"pith_short_8","alias_value":"UVZ32IUR","created_at":"2026-05-18T12:31:49.984773+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/UVZ32IURSJ7NOGUEM4WM5FEJWA","json":"https://pith.science/pith/UVZ32IURSJ7NOGUEM4WM5FEJWA.json","graph_json":"https://pith.science/api/pith-number/UVZ32IURSJ7NOGUEM4WM5FEJWA/graph.json","events_json":"https://pith.science/api/pith-number/UVZ32IURSJ7NOGUEM4WM5FEJWA/events.json","paper":"https://pith.science/paper/UVZ32IUR"},"agent_actions":{"view_html":"https://pith.science/pith/UVZ32IURSJ7NOGUEM4WM5FEJWA","download_json":"https://pith.science/pith/UVZ32IURSJ7NOGUEM4WM5FEJWA.json","view_paper":"https://pith.science/paper/UVZ32IUR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.01761&json=true","fetch_graph":"https://pith.science/api/pith-number/UVZ32IURSJ7NOGUEM4WM5FEJWA/graph.json","fetch_events":"https://pith.science/api/pith-number/UVZ32IURSJ7NOGUEM4WM5FEJWA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UVZ32IURSJ7NOGUEM4WM5FEJWA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UVZ32IURSJ7NOGUEM4WM5FEJWA/action/storage_attestation","attest_author":"https://pith.science/pith/UVZ32IURSJ7NOGUEM4WM5FEJWA/action/author_attestation","sign_citation":"https://pith.science/pith/UVZ32IURSJ7NOGUEM4WM5FEJWA/action/citation_signature","submit_replication":"https://pith.science/pith/UVZ32IURSJ7NOGUEM4WM5FEJWA/action/replication_record"}},"created_at":"2026-05-18T00:31:16.918914+00:00","updated_at":"2026-05-18T00:31:16.918914+00:00"}