{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009128848,0.0004268826,0.0004920933,0.0002191906,0.0002689674,0.0004758507,0.003421507,0.0002586378,0.00003249178],"category_scores_gemma":[0.002209996,0.0003236572,0.0002633636,0.0004006966,0.0001726357,0.0002371666,0.001129086,0.0014531,0.0001655478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008514239,"about_ca_system_score_gemma":0.0002493395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007466094,"about_ca_topic_score_gemma":0.000006889492,"domain_scores_codex":[0.9970931,0.00005355831,0.000707877,0.001106057,0.0006319871,0.0004074265],"domain_scores_gemma":[0.9964768,0.0003867889,0.00120659,0.0006435295,0.001194826,0.00009152557],"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":[0.00006510402,0.000146905,0.0001188742,0.0001869153,0.00002964044,1.289425e-7,0.001256898,0.01347113,0.001761015,0.9304497,0.0003940258,0.05211964],"study_design_scores_gemma":[0.0000349206,0.0005014383,0.00006534676,0.0006907271,0.00002229512,9.62029e-7,0.0004187447,0.7623204,0.03532018,0.1998264,0.000460164,0.0003384375],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0325617,0.00004691612,0.9373361,0.010068,0.002143065,0.002064843,0.00004704939,0.0003251723,0.01540716],"genre_scores_gemma":[0.9914502,0.00005990172,0.00690314,0.0001802513,0.0001408475,0.0001404697,0.0000186969,0.00002766298,0.001078811],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9588885,"threshold_uncertainty_score":0.9999216,"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."}}