{"id":"W2129068307","doi":"10.48550/arxiv.1412.4864","title":"Learning with Pseudo-Ensembles","year":2014,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":361,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Robustness (evolution); Artificial intelligence; Ensemble learning; Benchmark (surveying); Machine learning; Artificial neural network; Dropout (neural networks); Perturbation (astronomy); Pattern recognition (psychology); Algorithm","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":[0.002900076,0.001298412,0.00112892,0.0006126615,0.0007040671,0.001188718,0.002373664,0.001727757,0.001886336],"category_scores_gemma":[0.01104368,0.0008346945,0.00109114,0.0006281674,0.001556486,0.004198943,0.002916137,0.002388579,0.0009885324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007903403,"about_ca_system_score_gemma":0.001011119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002085315,"about_ca_topic_score_gemma":0.003647489,"domain_scores_codex":[0.9979438,0.0009024637,0.0001038428,0.0004684419,0.0004076733,0.0001737464],"domain_scores_gemma":[0.9950725,0.001608346,0.0003970137,0.001767905,0.0009063413,0.0002477863],"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":[0.0002493165,0.0001251703,0.003536611,0.00008910675,0.000165581,0.0001628136,0.000202671,0.8440339,0.005296802,0.04057922,0.005274846,0.100284],"study_design_scores_gemma":[0.000005349298,0.00002933437,0.00008382357,0.000003746032,0.000005548614,0.00002065866,0.000005201253,0.9872293,0.0006828096,0.01144079,0.0004880353,0.000005311254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03107834,0.0002058194,0.9655546,0.0002732526,0.00006888671,0.00004555436,0.0001558714,0.001301797,0.001315904],"genre_scores_gemma":[0.6507552,0.0002535544,0.3419908,0.0005703698,0.0002232726,0.0002863388,0.001430802,0.0005749814,0.003914655],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002900076,"threshold_uncertainty_score":0.01533729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06577836543410738,"score_gpt":0.2018170692429524,"score_spread":0.136038703808845,"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."}}