{"id":"W2116015303","doi":"10.1109/wacv.2014.6836049","title":"Bayesian Optimization with an Empirical Hardness Model for approximate Nearest Neighbour Search","year":2014,"lang":"en","type":"article","venue":"IEEE Winter Conference on Applications of Computer Vision","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Bayesian optimization; Computer science; Process (computing); Bayesian probability; Algorithm; Scale-invariant feature transform; Artificial intelligence; Mathematical optimization; Machine learning; Data mining; Mathematics; Feature extraction","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004350265,0.001292818,0.002579028,0.00142068,0.0008708846,0.001977542,0.00308134,0.002555284,0.00556154],"category_scores_gemma":[0.0254458,0.001025535,0.001585473,0.001401085,0.002590526,0.004422531,0.003263101,0.003388801,0.001336225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001734131,"about_ca_system_score_gemma":0.001496809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003904447,"about_ca_topic_score_gemma":0.003917962,"domain_scores_codex":[0.9969341,0.00130857,0.0001468848,0.0004082387,0.0009835425,0.0002186454],"domain_scores_gemma":[0.990606,0.006645332,0.0006188919,0.001027432,0.0008722363,0.0002301297],"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":[0.00007886411,0.00006183901,0.0005610077,0.0001061507,0.00004516238,0.00004823897,0.00005569008,0.9088752,0.0005595976,0.06566021,0.001709212,0.0222389],"study_design_scores_gemma":[0.00001044431,0.00001951847,0.00006651338,0.000008689365,0.000004217612,0.00001549799,0.000006602135,0.9681823,0.0001101873,0.03111797,0.0004511162,0.000007041529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004654511,0.0001877388,0.9929417,0.0002255828,0.00002093414,0.000035359,0.00004694682,0.0001553503,0.001731788],"genre_scores_gemma":[0.4549465,0.0008008265,0.5306674,0.0007415262,0.0002348857,0.0008701524,0.0007443997,0.0006168736,0.0103775],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00556154,"threshold_uncertainty_score":0.02300668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04160804252210753,"score_gpt":0.3515683237589217,"score_spread":0.3099602812368142,"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."}}