{"id":"W1506018867","doi":"10.1109/icassp.2015.7179021","title":"Online local Gaussian process for tensor-variate regression: Application to fast reconstruction of limb movements from brain signal","year":2015,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Gaussian process; Kriging; Computer science; Artificial intelligence; Regression; Tensor (intrinsic definition); Scalability; Machine learning; Random variate; Gaussian; Data set; Pattern recognition (psychology); Representation (politics); Data mining; Algorithm; Mathematics; Statistics; Random variable","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":[0.0001855937,0.0001693311,0.0002289368,0.0001097953,0.00006759361,0.00007451481,0.0006680829,0.00009402269,0.00002435804],"category_scores_gemma":[0.00004974499,0.0001309883,0.00004703688,0.0004392762,0.00004606376,0.0004533602,0.0001214906,0.00008094246,0.00001824417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004655282,"about_ca_system_score_gemma":0.0002072061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001131447,"about_ca_topic_score_gemma":0.00005749396,"domain_scores_codex":[0.9984838,0.00002668702,0.0004048019,0.0005246643,0.0003168769,0.0002432148],"domain_scores_gemma":[0.9986804,0.00005254537,0.0002311504,0.0003984854,0.0003982959,0.0002391796],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001861235,0.0004517206,0.001898149,0.0001118644,0.00003973328,0.000002170512,0.001188161,0.0009767601,0.005389617,0.02356398,0.001420494,0.9647712],"study_design_scores_gemma":[0.002655267,0.001485456,0.008559291,0.0004298426,0.00002494306,0.00003000284,0.001967929,0.7321667,0.06263301,0.1867175,0.002494376,0.0008356949],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0139748,0.00001949905,0.9817193,0.002906721,0.0001500338,0.0004313505,0.00004379571,0.00009142318,0.000663115],"genre_scores_gemma":[0.8457214,0.000001681752,0.1530551,0.000709872,0.0001221179,0.0000779928,0.00003523506,0.00001079431,0.0002658458],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9639356,"threshold_uncertainty_score":0.534155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02695918089549235,"score_gpt":0.2912321244973791,"score_spread":0.2642729436018867,"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."}}