{"id":"W2127099514","doi":"","title":"Large-Scale Learning of Embeddings with Reconstruction Sampling","year":2011,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Topic Modeling","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Vocabulary; Estimator; Context (archaeology); Sampling (signal processing); Artificial neural network; Scale (ratio); Encoder; Computer vision; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002770897,0.001387994,0.001437096,0.0009424351,0.0005260048,0.001172289,0.002139457,0.001609628,0.001918457],"category_scores_gemma":[0.01915213,0.000977012,0.0009217142,0.001114142,0.00120737,0.004573015,0.002694857,0.002515605,0.001243223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00076125,"about_ca_system_score_gemma":0.001124028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002501535,"about_ca_topic_score_gemma":0.003958491,"domain_scores_codex":[0.998131,0.0007459968,0.0001075631,0.0004518796,0.0004410966,0.0001224952],"domain_scores_gemma":[0.9932629,0.004112736,0.0003693937,0.001421269,0.0007003625,0.0001333052],"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":[0.0004480583,0.0002483708,0.003448387,0.0003172345,0.0001594991,0.0002767766,0.0003242351,0.5476606,0.01549974,0.04928024,0.008650067,0.3736868],"study_design_scores_gemma":[0.00002297004,0.00003345259,0.0001478036,0.000005981639,0.000007249652,0.0000373682,0.00001808448,0.9812322,0.00265136,0.01501794,0.0008175885,0.000007891834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007837283,0.0001031574,0.990684,0.0001084012,0.00002095529,0.0000298659,0.00004924332,0.0008926305,0.000274448],"genre_scores_gemma":[0.2696874,0.0002905718,0.7245209,0.0002902478,0.0001625747,0.0003723666,0.001453281,0.000359377,0.002863297],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002770897,"threshold_uncertainty_score":0.0146541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05268136800075558,"score_gpt":0.2867230285038735,"score_spread":0.234041660503118,"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."}}