{"id":"W4415724501","doi":"10.1002/sta4.70116","title":"Tensor Train Recurrent Network Language Model Prediction","year":2025,"lang":"en","type":"article","venue":"Stat","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Huawei Technologies","keywords":"Tensor (intrinsic definition); Convolutional neural network; Recurrent neural network; Matrix product state; Computation; Data compression; Reduction (mathematics); Artificial neural network","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":[0.00008776641,0.00006483078,0.00008602879,0.00003181961,0.00008549378,0.00001688586,0.00006750449,0.00003419879,0.00006384603],"category_scores_gemma":[0.00002434806,0.00005947551,0.00004294914,0.0001336278,0.00001702015,0.00002813136,0.00001685179,0.00008281906,0.00002634879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003083377,"about_ca_system_score_gemma":0.00001910314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001714173,"about_ca_topic_score_gemma":0.00001110787,"domain_scores_codex":[0.9995077,0.00002036044,0.0001499073,0.0001235165,0.00007359346,0.000124913],"domain_scores_gemma":[0.9996459,0.0000674175,0.00003286035,0.0001922423,0.00002981253,0.00003169404],"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":[0.00002365911,0.0001733338,0.0001041542,0.00005547329,0.00003607119,0.000001378637,0.001071306,0.001521938,0.001160559,0.5629283,0.4153531,0.01757067],"study_design_scores_gemma":[0.0006745333,0.00003235879,0.000858498,0.0001046453,0.00007912461,0.000003053487,0.0005751291,0.2260148,0.0004459043,0.7489743,0.02206388,0.0001737491],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1723539,0.0002928113,0.7455716,0.004054091,0.0004071489,0.0008629091,0.0004433666,0.0007666084,0.07524756],"genre_scores_gemma":[0.8730183,0.00004540143,0.1021252,0.001033801,0.0002397606,0.0002261303,0.000135806,0.00002986444,0.02314575],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7006644,"threshold_uncertainty_score":0.2425341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.034987884450652,"score_gpt":0.349536399725922,"score_spread":0.3145485152752701,"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."}}