{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:Y5O6NTLPDXUAT6QDHLBYBVVK24","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":"e8b04fd788b425730c6d5edb16dde3b3a451214b97447ead108d11cccf850a99","cross_cats_sorted":["cs.PF","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-16T17:13:00Z","title_canon_sha256":"8d289e87bcd7e6701da873a39477ba01243ba25de3a0e4917c0f8524cfd2b2af"},"schema_version":"1.0","source":{"id":"2003.07336","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2003.07336","created_at":"2026-07-05T00:54:48Z"},{"alias_kind":"arxiv_version","alias_value":"2003.07336v2","created_at":"2026-07-05T00:54:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.07336","created_at":"2026-07-05T00:54:48Z"},{"alias_kind":"pith_short_12","alias_value":"Y5O6NTLPDXUA","created_at":"2026-07-05T00:54:48Z"},{"alias_kind":"pith_short_16","alias_value":"Y5O6NTLPDXUAT6QD","created_at":"2026-07-05T00:54:48Z"},{"alias_kind":"pith_short_8","alias_value":"Y5O6NTLP","created_at":"2026-07-05T00:54:48Z"}],"graph_snapshots":[{"event_id":"sha256:22764c58361795bcc9b9d713a934b9dbda23404e8ccb7dd5ed8be4f6aa858129","target":"graph","created_at":"2026-07-05T00:54:48Z","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/2003.07336/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep learning-based recommendation models are used pervasively and broadly, for example, to recommend movies, products, or other information most relevant to users, in order to enhance the user experience. Among various application domains which have received significant industry and academia research attention, such as image classification, object detection, language and speech translation, the performance of deep learning-based recommendation models is less well explored, even though recommendation tasks unarguably represent significant AI inference cycles at large-scale datacenter fleets. T","authors_text":"Carole-Jean Wu, Ed H. Chi, Hao Zhang, Joseph Konstan, Julian McAuley, Robin Burke, Yves Raimond","cross_cats":["cs.PF","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-16T17:13:00Z","title":"Developing a Recommendation Benchmark for MLPerf Training and Inference"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.07336","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":"sha256:9ac26d9b21f91db28ca59ba389212ca497a9b77acb590f3e616bd4cd6630fa30","target":"record","created_at":"2026-07-05T00:54:48Z","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":"e8b04fd788b425730c6d5edb16dde3b3a451214b97447ead108d11cccf850a99","cross_cats_sorted":["cs.PF","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-16T17:13:00Z","title_canon_sha256":"8d289e87bcd7e6701da873a39477ba01243ba25de3a0e4917c0f8524cfd2b2af"},"schema_version":"1.0","source":{"id":"2003.07336","kind":"arxiv","version":2}},"canonical_sha256":"c75de6cd6f1de809fa033ac380d6aad70f8333b21ffa6af966bef4368859eba0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c75de6cd6f1de809fa033ac380d6aad70f8333b21ffa6af966bef4368859eba0","first_computed_at":"2026-07-05T00:54:48.106115Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:54:48.106115Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TtWVt31n7YnWNw9LmPt+Uyob9dml4ZwvvozzPex6A7PuGOm0PFuF9MhiEw8DwU64hczzWAHZM7Tg/zQBc/SCCg==","signature_status":"signed_v1","signed_at":"2026-07-05T00:54:48.106563Z","signed_message":"canonical_sha256_bytes"},"source_id":"2003.07336","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9ac26d9b21f91db28ca59ba389212ca497a9b77acb590f3e616bd4cd6630fa30","sha256:22764c58361795bcc9b9d713a934b9dbda23404e8ccb7dd5ed8be4f6aa858129"],"state_sha256":"c32b38c4aed6d15dd4ff8419857b91588043c03b27e5d0447b4cc5e6882fc0e4"}