{"id":"W3159394092","doi":"","title":"CLAR: Contrastive Learning of Auditory Representations","year":2021,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence and Statistics","topic":"Music and Audio Processing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Artificial intelligence; Supervised learning; Representation (politics); Machine learning; Speech recognition; Quality (philosophy); Raw data; Labeled data; Training set; Natural language processing; Artificial neural network","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":[0.0001578486,0.00008911954,0.0001266805,0.00007122126,0.0001121495,0.0001827346,0.0002489491,0.00003918114,0.0002506865],"category_scores_gemma":[0.000676765,0.0000912804,0.00002409876,0.0001294379,0.0001384332,0.000187334,0.00009677031,0.0001660012,0.00002818344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001678631,"about_ca_system_score_gemma":0.0001759581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001666286,"about_ca_topic_score_gemma":0.00001951928,"domain_scores_codex":[0.9989316,0.00007543496,0.0003104716,0.0002738225,0.0002889109,0.0001196896],"domain_scores_gemma":[0.998585,0.0003876911,0.0001521231,0.0001250467,0.0006950709,0.00005501002],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001068779,0.0000462004,0.00007777783,0.000005962039,0.00001682038,0.00002466702,0.000613279,0.0003732229,0.003330081,0.8199154,0.0002376457,0.1753482],"study_design_scores_gemma":[0.00005358291,0.0001222475,0.0009375698,0.0001149401,0.00001066111,0.00002280397,0.001774718,0.4106809,0.04131344,0.5435926,0.001165805,0.0002107954],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001478564,0.00002748203,0.9833483,0.001630607,0.0005942264,0.00003667431,0.00003531698,0.00002455314,0.01282428],"genre_scores_gemma":[0.9493397,0.0001358538,0.04962329,0.0002555744,0.00009983134,0.00000443451,0.00002067483,0.00000402075,0.000516627],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9478611,"threshold_uncertainty_score":0.3722307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1028174873514848,"score_gpt":0.3563111225016691,"score_spread":0.2534936351501842,"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."}}