{"id":"W2951706336","doi":"10.48550/arxiv.1306.2663","title":"Large Margin Low Rank Tensor Analysis","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Tensor (intrinsic definition); Generalization; Computer science; Artificial intelligence; Nonlinear dimensionality reduction; Manifold (fluid mechanics); Margin (machine learning); Pattern recognition (psychology); Curse of dimensionality; Dimensionality reduction; Similarity (geometry); Rank (graph theory); Euclidean space; Object (grammar); Cognition; Feature vector; Mathematics; Machine learning; Pure mathematics; Image (mathematics); Psychology","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000175982,0.0003341174,0.0005850491,0.0004811211,0.0001851547,0.00008117764,0.0006752769,0.000343397,0.002398526],"category_scores_gemma":[0.000039559,0.0003683052,0.0006521488,0.000936299,0.0000750433,0.00009923756,0.0005251737,0.0005199623,0.0009610866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001429269,"about_ca_system_score_gemma":0.00004956072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006510184,"about_ca_topic_score_gemma":0.00006968176,"domain_scores_codex":[0.9982979,0.0001205799,0.0002907123,0.0008314209,0.00009701624,0.0003623911],"domain_scores_gemma":[0.9976915,0.0001940427,0.0003282282,0.001343545,0.0002400677,0.0002026752],"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":[0.00003485754,0.0008129011,0.005297218,0.0002361814,0.002275734,0.0000985786,0.0002923797,0.01490392,0.00009434471,0.9592628,0.01663578,0.00005526055],"study_design_scores_gemma":[0.00168993,0.00002551094,0.009797212,0.0001176105,0.005581333,0.000004658589,0.0005606117,0.2902488,0.0001744508,0.6838193,0.006587183,0.001393376],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7923524,0.00001821594,0.1989341,0.0002411755,0.0001070463,0.0005970772,0.0002616289,0.0003688305,0.007119576],"genre_scores_gemma":[0.9827464,0.00006900725,0.002059375,0.0001371203,0.00007243777,0.000007652194,0.0001686181,0.00003631536,0.01470312],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2754436,"threshold_uncertainty_score":0.9998769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08061920354587528,"score_gpt":0.2293455989999257,"score_spread":0.1487263954540504,"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."}}