{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2013:SZRCRRLA72SLMHNJFOTWLXWIT2","short_pith_number":"pith:SZRCRRLA","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"},"canonical_sha256":"966228c560fea4b61da92ba765dec89eb5ea6dd2ed5dff7e44da90736f9ee693","source":{"kind":"arxiv","id":"1306.3476","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1306.3476","created_at":"2026-05-18T03:20:57Z"},{"alias_kind":"arxiv_version","alias_value":"1306.3476v1","created_at":"2026-05-18T03:20:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1306.3476","created_at":"2026-05-18T03:20:57Z"},{"alias_kind":"pith_short_12","alias_value":"SZRCRRLA72SL","created_at":"2026-05-18T12:27:59Z"},{"alias_kind":"pith_short_16","alias_value":"SZRCRRLA72SLMHNJ","created_at":"2026-05-18T12:27:59Z"},{"alias_kind":"pith_short_8","alias_value":"SZRCRRLA","created_at":"2026-05-18T12:27:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2013:SZRCRRLA72SLMHNJFOTWLXWIT2","target":"record","payload":{"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"},"canonical_sha256":"966228c560fea4b61da92ba765dec89eb5ea6dd2ed5dff7e44da90736f9ee693","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"},"source_kind":"arxiv","source_id":"1306.3476","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:20:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AlqjFO/GW3c+s/7JIhIwGbUb3JQrfutZzLQVo4u9L4OYDuz0IS4CMj5XThOoK/SoU+3DN6A0fLuVNbBG14BtBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T12:00:24.286788Z"},"content_sha256":"189d865f1b235c1997b94e9ba4818ca78d63d994235baaa6b014ce606a34684c","schema_version":"1.0","event_id":"sha256:189d865f1b235c1997b94e9ba4818ca78d63d994235baaa6b014ce606a34684c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2013:SZRCRRLA72SLMHNJFOTWLXWIT2","target":"graph","payload":{"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:20:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rIqf+cvYQnGccxuEVpn+TKitDJLX151w0DATzYA9iTZS6UVKvDGx83xSWAak7aZ/opXh/FY36GwW6yWo+9gKBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T12:00:24.287386Z"},"content_sha256":"8c8af773591b9f353ca5f4ffbfa948c48f63e4b1e5b79f3c8c34837903484935","schema_version":"1.0","event_id":"sha256:8c8af773591b9f353ca5f4ffbfa948c48f63e4b1e5b79f3c8c34837903484935"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SZRCRRLA72SLMHNJFOTWLXWIT2/bundle.json","state_url":"https://pith.science/pith/SZRCRRLA72SLMHNJFOTWLXWIT2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SZRCRRLA72SLMHNJFOTWLXWIT2/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-07T12:00:24Z","links":{"resolver":"https://pith.science/pith/SZRCRRLA72SLMHNJFOTWLXWIT2","bundle":"https://pith.science/pith/SZRCRRLA72SLMHNJFOTWLXWIT2/bundle.json","state":"https://pith.science/pith/SZRCRRLA72SLMHNJFOTWLXWIT2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SZRCRRLA72SLMHNJFOTWLXWIT2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:SZRCRRLA72SLMHNJFOTWLXWIT2","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":"4aba609ec268e5803cdb0d2448189845c9659e75255862e556b7f6de95494c76","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2013-06-14T18:28:52Z","title_canon_sha256":"2c30dc938e80c43207382c1167610d0900464252cf3c9634f43339a69a1cf08c"},"schema_version":"1.0","source":{"id":"1306.3476","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1306.3476","created_at":"2026-05-18T03:20:57Z"},{"alias_kind":"arxiv_version","alias_value":"1306.3476v1","created_at":"2026-05-18T03:20:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1306.3476","created_at":"2026-05-18T03:20:57Z"},{"alias_kind":"pith_short_12","alias_value":"SZRCRRLA72SL","created_at":"2026-05-18T12:27:59Z"},{"alias_kind":"pith_short_16","alias_value":"SZRCRRLA72SLMHNJ","created_at":"2026-05-18T12:27:59Z"},{"alias_kind":"pith_short_8","alias_value":"SZRCRRLA","created_at":"2026-05-18T12:27:59Z"}],"graph_snapshots":[{"event_id":"sha256:8c8af773591b9f353ca5f4ffbfa948c48f63e4b1e5b79f3c8c34837903484935","target":"graph","created_at":"2026-05-18T03:20:57Z","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":"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","authors_text":"David D. Cox, James Bergstra","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2013-06-14T18:28:52Z","title":"Hyperparameter Optimization and Boosting for Classifying Facial Expressions: How good can a \"Null\" Model be?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1306.3476","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":"sha256:189d865f1b235c1997b94e9ba4818ca78d63d994235baaa6b014ce606a34684c","target":"record","created_at":"2026-05-18T03:20:57Z","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":"4aba609ec268e5803cdb0d2448189845c9659e75255862e556b7f6de95494c76","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2013-06-14T18:28:52Z","title_canon_sha256":"2c30dc938e80c43207382c1167610d0900464252cf3c9634f43339a69a1cf08c"},"schema_version":"1.0","source":{"id":"1306.3476","kind":"arxiv","version":1}},"canonical_sha256":"966228c560fea4b61da92ba765dec89eb5ea6dd2ed5dff7e44da90736f9ee693","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"966228c560fea4b61da92ba765dec89eb5ea6dd2ed5dff7e44da90736f9ee693","first_computed_at":"2026-05-18T03:20:57.587955Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:20:57.587955Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1fv7Kzi82HsQcM3mJrqgL+kIUF6iwYkHTUsA749OFMhU91sHXewB8S/3cXmuJoIBN+8hQvJeC5YiqXtZ4NtVCg==","signature_status":"signed_v1","signed_at":"2026-05-18T03:20:57.588577Z","signed_message":"canonical_sha256_bytes"},"source_id":"1306.3476","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:189d865f1b235c1997b94e9ba4818ca78d63d994235baaa6b014ce606a34684c","sha256:8c8af773591b9f353ca5f4ffbfa948c48f63e4b1e5b79f3c8c34837903484935"],"state_sha256":"2ee1d17c8965838a4dbde4c556635afa427d5e7f482e174fd56c9d0358a37e8b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lAcfdg78CtLGKwSQaldbJs6h/LnIaKjfJOMDTfE4oUkWoBbw94X7K3JMixhhYmU/pjfEB4X56m9n64wD5d2AAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T12:00:24.290273Z","bundle_sha256":"d0ba6ebde70efea8f25be2c8d7da1d704b1e37a38f9fe1adccb52d9bba47c52e"}}