{"id":"W4408503389","doi":"10.21468/scipostphys.18.3.096","title":"Generative learning of continuous data by tensor networks","year":2025,"lang":"en","type":"article","venue":"SciPost Physics","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; Université de Montréal; Perimeter Institute","funders":"Canadian Institute for Advanced Research","keywords":"Generative grammar; Computer science; Tensor (intrinsic definition); Artificial intelligence; Mathematics; Geometry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001127067,0.00009391012,0.0001976107,0.00001816597,0.0001252242,0.00002677699,0.0003159015,0.00004218917,0.00002319648],"category_scores_gemma":[0.00005820495,0.00008925647,0.00003806993,0.0002469526,0.00007458239,0.00008042068,0.0001587525,0.0001569932,0.00001173665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001240939,"about_ca_system_score_gemma":0.00002466628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006362468,"about_ca_topic_score_gemma":0.000001768002,"domain_scores_codex":[0.9993004,0.00004939908,0.0002101484,0.0002134782,0.00009878629,0.000127808],"domain_scores_gemma":[0.9989784,0.0002107127,0.0001274973,0.000534918,0.0001196441,0.00002877099],"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":[0.00002040992,0.0004957283,0.0009154094,0.00005979449,0.0001875222,8.008972e-7,0.0003207493,0.0008776584,0.02553101,0.6733859,0.2823649,0.01584016],"study_design_scores_gemma":[0.002487118,0.0001882945,0.001280395,0.0003641858,0.0005851269,0.000004674904,0.001234871,0.329341,0.06893877,0.4710957,0.1234896,0.0009903021],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07072933,0.0002913547,0.910775,0.001536681,0.000183248,0.0005438469,0.0003028427,0.0002259952,0.01541168],"genre_scores_gemma":[0.9742011,0.00003091435,0.01882612,0.0003459404,0.0001517018,0.00002575611,0.0004802296,0.0000246408,0.005913607],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9034718,"threshold_uncertainty_score":0.3639773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04274866320332639,"score_gpt":0.344044435391464,"score_spread":0.3012957721881376,"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."}}