{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:SZRCRRLA72SLMHNJFOTWLXWIT2","short_pith_number":"pith:SZRCRRLA","schema_version":"1.0","canonical_sha256":"966228c560fea4b61da92ba765dec89eb5ea6dd2ed5dff7e44da90736f9ee693","source":{"kind":"arxiv","id":"1306.3476","version":1},"attestation_state":"computed","paper":{"title":"Hyperparameter Optimization and Boosting for Classifying Facial Expressions: How good can a \"Null\" Model be?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"David D. Cox, James Bergstra","submitted_at":"2013-06-14T18:28:52Z","abstract_excerpt":"One of the goals of the ICML workshop on representation and learning is to establish benchmark scores for a new data set of labeled facial expressions. This paper presents the performance of a \"Null\" model consisting of convolutions with random weights, PCA, pooling, normalization, and a linear readout. Our approach focused on hyperparameter optimization rather than novel model components. On the Facial Expression Recognition Challenge held by the Kaggle website, our hyperparameter optimization approach achieved a score of 60% accuracy on the test data. This paper also introduces a new ensembl"},"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":"1306.3476","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2013-06-14T18:28:52Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"2c30dc938e80c43207382c1167610d0900464252cf3c9634f43339a69a1cf08c","abstract_canon_sha256":"4aba609ec268e5803cdb0d2448189845c9659e75255862e556b7f6de95494c76"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:20:57.588577Z","signature_b64":"1fv7Kzi82HsQcM3mJrqgL+kIUF6iwYkHTUsA749OFMhU91sHXewB8S/3cXmuJoIBN+8hQvJeC5YiqXtZ4NtVCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"966228c560fea4b61da92ba765dec89eb5ea6dd2ed5dff7e44da90736f9ee693","last_reissued_at":"2026-05-18T03:20:57.587955Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:20:57.587955Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hyperparameter Optimization and Boosting for Classifying Facial Expressions: How good can a \"Null\" Model be?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"David D. Cox, James Bergstra","submitted_at":"2013-06-14T18:28:52Z","abstract_excerpt":"One of the goals of the ICML workshop on representation and learning is to establish benchmark scores for a new data set of labeled facial expressions. This paper presents the performance of a \"Null\" model consisting of convolutions with random weights, PCA, pooling, normalization, and a linear readout. Our approach focused on hyperparameter optimization rather than novel model components. On the Facial Expression Recognition Challenge held by the Kaggle website, our hyperparameter optimization approach achieved a score of 60% accuracy on the test data. This paper also introduces a new ensembl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1306.3476","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":"1306.3476","created_at":"2026-05-18T03:20:57.588066+00:00"},{"alias_kind":"arxiv_version","alias_value":"1306.3476v1","created_at":"2026-05-18T03:20:57.588066+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1306.3476","created_at":"2026-05-18T03:20:57.588066+00:00"},{"alias_kind":"pith_short_12","alias_value":"SZRCRRLA72SL","created_at":"2026-05-18T12:27:59.945178+00:00"},{"alias_kind":"pith_short_16","alias_value":"SZRCRRLA72SLMHNJ","created_at":"2026-05-18T12:27:59.945178+00:00"},{"alias_kind":"pith_short_8","alias_value":"SZRCRRLA","created_at":"2026-05-18T12:27:59.945178+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/SZRCRRLA72SLMHNJFOTWLXWIT2","json":"https://pith.science/pith/SZRCRRLA72SLMHNJFOTWLXWIT2.json","graph_json":"https://pith.science/api/pith-number/SZRCRRLA72SLMHNJFOTWLXWIT2/graph.json","events_json":"https://pith.science/api/pith-number/SZRCRRLA72SLMHNJFOTWLXWIT2/events.json","paper":"https://pith.science/paper/SZRCRRLA"},"agent_actions":{"view_html":"https://pith.science/pith/SZRCRRLA72SLMHNJFOTWLXWIT2","download_json":"https://pith.science/pith/SZRCRRLA72SLMHNJFOTWLXWIT2.json","view_paper":"https://pith.science/paper/SZRCRRLA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1306.3476&json=true","fetch_graph":"https://pith.science/api/pith-number/SZRCRRLA72SLMHNJFOTWLXWIT2/graph.json","fetch_events":"https://pith.science/api/pith-number/SZRCRRLA72SLMHNJFOTWLXWIT2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SZRCRRLA72SLMHNJFOTWLXWIT2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SZRCRRLA72SLMHNJFOTWLXWIT2/action/storage_attestation","attest_author":"https://pith.science/pith/SZRCRRLA72SLMHNJFOTWLXWIT2/action/author_attestation","sign_citation":"https://pith.science/pith/SZRCRRLA72SLMHNJFOTWLXWIT2/action/citation_signature","submit_replication":"https://pith.science/pith/SZRCRRLA72SLMHNJFOTWLXWIT2/action/replication_record"}},"created_at":"2026-05-18T03:20:57.588066+00:00","updated_at":"2026-05-18T03:20:57.588066+00:00"}