{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003583969,0.0001438782,0.0001644055,0.0002057377,0.0001242338,0.00007742404,0.000595931,0.00005407136,0.0003703275],"category_scores_gemma":[0.0001001725,0.0001294473,0.00004952075,0.0001378219,0.00003667433,0.0004175194,0.0001561264,0.0005539918,0.00002554159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003925078,"about_ca_system_score_gemma":0.00004862518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001055653,"about_ca_topic_score_gemma":0.00002384932,"domain_scores_codex":[0.9986982,0.0000787962,0.0002760703,0.000380463,0.000360605,0.0002058812],"domain_scores_gemma":[0.9991653,0.00005449378,0.0002760472,0.0001927083,0.0002551269,0.00005629988],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001676142,0.0001434235,0.1881627,0.00003087159,0.0001219392,0.00001741065,0.01460992,0.0254453,0.005070835,0.6290587,0.00000444584,0.1371668],"study_design_scores_gemma":[0.0004402932,0.0002311332,0.002643672,0.0001775786,0.000005613534,0.00005379999,0.0006614334,0.9902681,0.001512933,0.003189974,0.0006219773,0.0001934999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2085151,0.000009490717,0.7598861,0.0001601534,0.0003086149,0.00005318958,0.000001222851,0.0001370009,0.03092911],"genre_scores_gemma":[0.8971248,0.00001425703,0.1021198,0.00004111136,0.00004635418,0.000005494262,0.000005794388,0.0000111052,0.0006313499],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9648228,"threshold_uncertainty_score":0.5278708,"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."}}