{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:TGXGQ4PCCLAM5TMLICJRO6A6DI","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"7c402ad1c258d3d6582352394fa4b17aa4e5c82b04162642504b45d174bf1485","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-20T19:03:44Z","title_canon_sha256":"4396c30e8ac9bbb433010864b4233a6d836bbb4c5966483063d2d107b87ba880"},"schema_version":"1.0","source":{"id":"2007.10412","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2007.10412","created_at":"2026-07-05T02:22:37Z"},{"alias_kind":"arxiv_version","alias_value":"2007.10412v2","created_at":"2026-07-05T02:22:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.10412","created_at":"2026-07-05T02:22:37Z"},{"alias_kind":"pith_short_12","alias_value":"TGXGQ4PCCLAM","created_at":"2026-07-05T02:22:37Z"},{"alias_kind":"pith_short_16","alias_value":"TGXGQ4PCCLAM5TML","created_at":"2026-07-05T02:22:37Z"},{"alias_kind":"pith_short_8","alias_value":"TGXGQ4PC","created_at":"2026-07-05T02:22:37Z"}],"graph_snapshots":[{"event_id":"sha256:649565401c08301e7a222c6a18f8f89ca5db3a48b5b4f8fbafa6b3b75bf19a0f","target":"graph","created_at":"2026-07-05T02:22:37Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2007.10412/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The successes of deep learning, variational inference, and many other fields have been aided by specialized implementations of reverse-mode automatic differentiation (AD) to compute gradients of mega-dimensional objectives. The AD techniques underlying these tools were designed to compute exact gradients to numerical precision, but modern machine learning models are almost always trained with stochastic gradient descent. Why spend computation and memory on exact (minibatch) gradients only to use them for stochastic optimization? We develop a general framework and approach for randomized automa","authors_text":"Alex Beatson, Deniz Oktay, Joshua Aduol, Nick McGreivy, Ryan P. Adams","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-20T19:03:44Z","title":"Randomized Automatic Differentiation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.10412","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:7c344836273fa503bbc09bbf9a654e796d72ada34d75f73eb0d700ffa2595687","target":"record","created_at":"2026-07-05T02:22:37Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"7c402ad1c258d3d6582352394fa4b17aa4e5c82b04162642504b45d174bf1485","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-20T19:03:44Z","title_canon_sha256":"4396c30e8ac9bbb433010864b4233a6d836bbb4c5966483063d2d107b87ba880"},"schema_version":"1.0","source":{"id":"2007.10412","kind":"arxiv","version":2}},"canonical_sha256":"99ae6871e212c0cecd8b409317781e1a3c7c9e032988aad61ecbf6404a715b2b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"99ae6871e212c0cecd8b409317781e1a3c7c9e032988aad61ecbf6404a715b2b","first_computed_at":"2026-07-05T02:22:37.221651Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:22:37.221651Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dn52DTO5fUhKaPq84xDYIa+CmEH5L6G75xdhWrvnHi4UnqoO79UGYjvOK1RbcYct8OxKogXU+VnBPA4vXhnTCw==","signature_status":"signed_v1","signed_at":"2026-07-05T02:22:37.222192Z","signed_message":"canonical_sha256_bytes"},"source_id":"2007.10412","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7c344836273fa503bbc09bbf9a654e796d72ada34d75f73eb0d700ffa2595687","sha256:649565401c08301e7a222c6a18f8f89ca5db3a48b5b4f8fbafa6b3b75bf19a0f"],"state_sha256":"9c1de8b797056829bad3e39cf0fa10e77a3925174ffb7e3c2f038c9e2a66b6e3"}