{"id":"W2887615774","doi":"10.1609/aaai.v33i01.33013470","title":"On-Line Adaptative Curriculum Learning for GANs","year":2019,"lang":"en","type":"preprint","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; McGill University","funders":"","keywords":"Discriminator; Computer science; Generator (circuit theory); Generative grammar; Adversarial system; Artificial intelligence; Cover (algebra); Machine learning; Curriculum; Convergence (economics); Quality (philosophy)","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":[0.001757866,0.001305321,0.001017544,0.0005758645,0.0004166698,0.0008607407,0.002077857,0.001366305,0.003621907],"category_scores_gemma":[0.006411001,0.0006202071,0.0006292048,0.0004645399,0.001042183,0.001776424,0.001917342,0.002747016,0.001021114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001316773,"about_ca_system_score_gemma":0.0007944809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002176815,"about_ca_topic_score_gemma":0.003705047,"domain_scores_codex":[0.9992985,0.0002861816,0.00002370058,0.0001851611,0.0001219288,0.00008451263],"domain_scores_gemma":[0.9981925,0.001165808,0.0001318168,0.0002313625,0.0001762905,0.0001021195],"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":[0.00008967759,0.00007134498,0.0008787647,0.00005610409,0.00003841746,0.00005845169,0.00006921578,0.9124894,0.002069412,0.01426612,0.002273715,0.0676394],"study_design_scores_gemma":[0.000004460112,0.00001221742,0.00004616807,0.000004993723,0.00000239998,0.00001089496,0.000003894326,0.9932555,0.0003576575,0.006019855,0.0002796057,0.000002289518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01661557,0.000253951,0.9793058,0.0002387269,0.00003822991,0.00007354202,0.00008181436,0.001110111,0.002282309],"genre_scores_gemma":[0.7433832,0.0003476008,0.2461919,0.0006684803,0.0001030418,0.0004206552,0.0006505639,0.0004932171,0.007741436],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003621907,"threshold_uncertainty_score":0.01211649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.113623767422601,"score_gpt":0.3441957825344797,"score_spread":0.2305720151118786,"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."}}