{"id":"W2119985666","doi":"","title":"Inferring Motor Programs from Images of Handwritten Digits","year":2005,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"MNIST database; Overfitting; Computer science; Digit recognition; Artificial intelligence; Classifier (UML); Set (abstract data type); Pattern recognition (psychology); Class (philosophy); Numerical digit; Machine learning; Speech recognition; Artificial neural network; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008293561,0.0001209139,0.0001627322,0.0001183801,0.00006778981,0.0003238264,0.0001022017,0.00006390108,0.0000106033],"category_scores_gemma":[0.00002286715,0.000113228,0.00003440302,0.0001545522,0.00001642057,0.002857862,0.00001460065,0.0001218996,0.00005074549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003858403,"about_ca_system_score_gemma":0.00001087149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003123598,"about_ca_topic_score_gemma":0.000001527004,"domain_scores_codex":[0.9990399,0.0000131021,0.0005198175,0.00006160043,0.0002109908,0.0001546196],"domain_scores_gemma":[0.9995728,0.00001543584,0.0001578393,0.00009956751,0.0001053417,0.00004897701],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000521114,0.000006971934,0.0028443,0.0004189117,0.00001197275,2.287467e-7,0.00169531,0.7579671,0.001627825,0.00003313439,0.0001671635,0.2352219],"study_design_scores_gemma":[0.0002399674,0.00001454355,0.003610796,0.000187438,0.000006320271,0.00000550551,0.0002954076,0.9856609,0.00117811,0.000002694941,0.008658101,0.0001401867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.895427,0.001275467,0.08548619,0.0001090807,0.0007709008,0.0006745901,0.00000822656,0.001734406,0.01451411],"genre_scores_gemma":[0.9990698,0.000004838651,0.0005353488,0.00002730352,0.0001604646,0.00002388292,0.00006448807,0.000014396,0.00009951482],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2350817,"threshold_uncertainty_score":0.4617303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01644595604666064,"score_gpt":0.2193960317840821,"score_spread":0.2029500757374215,"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."}}