{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:JE2JMKP4P35LPRRECN5L573CKK","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":"8de546623534633687c78448274fbc179fb803cfb190eaeb5f73c136b54dbea1","cross_cats_sorted":["math.OC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-17T17:31:59Z","title_canon_sha256":"8cf91bce61e1452898c17003e4ad8d342f44865bcf7612b250afcf5483256424"},"schema_version":"1.0","source":{"id":"1807.06574","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.06574","created_at":"2026-05-18T00:10:32Z"},{"alias_kind":"arxiv_version","alias_value":"1807.06574v1","created_at":"2026-05-18T00:10:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.06574","created_at":"2026-05-18T00:10:32Z"},{"alias_kind":"pith_short_12","alias_value":"JE2JMKP4P35L","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"JE2JMKP4P35LPRRE","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"JE2JMKP4","created_at":"2026-05-18T12:32:31Z"}],"graph_snapshots":[{"event_id":"sha256:100a2364e3d3574f77c3dd8eacedb00d9f19f005f12bccf5d9cd82e8713aae36","target":"graph","created_at":"2026-05-18T00:10:32Z","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"},"paper":{"abstract_excerpt":"This paper introduces Jensen, an easily extensible and scalable toolkit for production-level machine learning and convex optimization. Jensen implements a framework of convex (or loss) functions, convex optimization algorithms (including Gradient Descent, L-BFGS, Stochastic Gradient Descent, Conjugate Gradient, etc.), and a family of machine learning classifiers and regressors (Logistic Regression, SVMs, Least Square Regression, etc.). This framework makes it possible to deploy and train models with a few lines of code, and also extend and build upon this by integrating new loss functions and ","authors_text":"John T. Halloran, Kai Wei, Rishabh Iyer","cross_cats":["math.OC","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-17T17:31:59Z","title":"Jensen: An Easily-Extensible C++ Toolkit for Production-Level Machine Learning and Convex Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.06574","kind":"arxiv","version":1},"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":"sha256:344a4d18e36b6ef5ae727c5bb8da84050deb92bc44d0578ff7b375714d79d5be","target":"record","created_at":"2026-05-18T00:10:32Z","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":"8de546623534633687c78448274fbc179fb803cfb190eaeb5f73c136b54dbea1","cross_cats_sorted":["math.OC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-17T17:31:59Z","title_canon_sha256":"8cf91bce61e1452898c17003e4ad8d342f44865bcf7612b250afcf5483256424"},"schema_version":"1.0","source":{"id":"1807.06574","kind":"arxiv","version":1}},"canonical_sha256":"49349629fc7efab7c624137abeff625287e335998ad3089d05a4e06eec014323","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"49349629fc7efab7c624137abeff625287e335998ad3089d05a4e06eec014323","first_computed_at":"2026-05-18T00:10:32.647952Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:10:32.647952Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4129jHbHiAfcmyaGqxaTj2Uz7rs8PDCN8bU8l4hf+q8A5jQ8FN4kt400FLH497EZaD9Cr1lPvR9cEVaKBUCoBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:10:32.648623Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.06574","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:344a4d18e36b6ef5ae727c5bb8da84050deb92bc44d0578ff7b375714d79d5be","sha256:100a2364e3d3574f77c3dd8eacedb00d9f19f005f12bccf5d9cd82e8713aae36"],"state_sha256":"5421504a4adfbf5c63f0c4a74f01934456d94bcf188a0a27f6064ce4561fdff8"}