{"id":"W2157617585","doi":"","title":"Learning to Disentangle Factors of Variation with Manifold Interaction","year":2014,"lang":"en","type":"article","venue":"","topic":"Face recognition and analysis","field":"Computer Science","cited_by":213,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Variation (astronomy); Artificial intelligence; ENCODE; Invariant (physics); Exploit; Boltzmann machine; Multiplicative function; Pattern recognition (psychology); Face (sociological concept); Machine learning; Task analysis; Facial recognition system; Task (project management); Deep learning; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006490413,0.00003941793,0.00006109774,0.00008547911,0.00002945496,0.00003783451,0.00008736161,0.00001059563,0.00007687535],"category_scores_gemma":[0.00002725692,0.00002814738,0.00002064801,0.0002806435,0.000002175878,0.0002120473,0.00002529455,0.00003144718,0.00004390366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001169656,"about_ca_system_score_gemma":0.000004533645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007337236,"about_ca_topic_score_gemma":0.00003887202,"domain_scores_codex":[0.999626,0.00002972896,0.00007495513,0.0001096564,0.0001040742,0.00005554619],"domain_scores_gemma":[0.9997358,0.00003532631,0.00004419498,0.00008918981,0.00005865195,0.00003689295],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007042342,0.0007417196,0.1854871,0.0001033334,0.0003784914,0.000003740541,0.02081586,0.04023322,0.09378517,0.1924911,0.001204751,0.4646851],"study_design_scores_gemma":[0.0005144127,0.0008583718,0.0972371,0.0000725201,0.00004465561,0.000004446658,0.001540511,0.8197933,0.07351068,0.0005743852,0.005457293,0.0003923405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2495773,2.957733e-7,0.7462067,0.0002188529,0.00003392817,0.00002224295,9.843059e-8,0.0000444386,0.003896108],"genre_scores_gemma":[0.9906349,5.983919e-7,0.008647563,0.00007056849,0.000008491789,0.000001217871,0.000002165153,0.000001990615,0.0006325147],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7795601,"threshold_uncertainty_score":0.1147817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009805998279506383,"score_gpt":0.2237740320333088,"score_spread":0.2139680337538025,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}