{"id":"W4392120655","doi":"10.48550/arxiv.2402.14285","title":"Symbolic Music Generation with Non-Differentiable Rule Guided Diffusion","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Music and Audio Processing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research; California Institute of Technology; National Science Foundation","keywords":"Differentiable function; Diffusion; Computer science; The Symbolic; Mathematics; Psychology; Pure mathematics; Physics; Thermodynamics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005838833,0.0006391326,0.0005423142,0.000286103,0.0002541251,0.0008489015,0.0009937444,0.0008240784,0.003600788],"category_scores_gemma":[0.002854462,0.0003000523,0.0004539551,0.0003190387,0.0006995104,0.0008704948,0.001089995,0.001324057,0.001087229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004257533,"about_ca_system_score_gemma":0.000574764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002384855,"about_ca_topic_score_gemma":0.003352293,"domain_scores_codex":[0.9996771,0.0000594072,0.00001559755,0.00009554889,0.0001157691,0.00003668306],"domain_scores_gemma":[0.9992996,0.0003920489,0.00006138172,0.0001057288,0.00008845099,0.00005273376],"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":[0.0001552435,0.00009176681,0.000802157,0.0001206345,0.00004179368,0.000191943,0.0001546215,0.7317832,0.02573032,0.02917981,0.00274892,0.2089996],"study_design_scores_gemma":[0.0000113184,0.00001837846,0.00004119076,0.000003703843,0.000002882754,0.00001675989,0.000003917447,0.9922512,0.002154453,0.004709445,0.0007825732,0.000004245785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01969069,0.0003252324,0.9740249,0.0002159843,0.00007647256,0.0000387753,0.00007266888,0.001854786,0.003700531],"genre_scores_gemma":[0.7350136,0.0003392839,0.2537163,0.000225901,0.00007328689,0.0001195615,0.0003172787,0.0005525723,0.009642267],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003600788,"threshold_uncertainty_score":0.0120458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07703061971634938,"score_gpt":0.1841029751977205,"score_spread":0.1070723554813711,"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."}}