{"id":"W4287021065","doi":"10.48550/arxiv.2108.10825","title":"Adaptive Group Lasso Neural Network Models for Functions of Few Variables and Time-Dependent Data","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Dalhousie University; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Artificial neural network; Lasso (programming language); Constraint (computer-aided design); Penalty method; Computer science; Function (biology); Algorithm; Mathematical optimization; Artificial intelligence; Mathematics","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":[0.002747553,0.001576061,0.001558923,0.0004507623,0.0004337237,0.001126871,0.002121298,0.001768046,0.001956512],"category_scores_gemma":[0.005674965,0.0005873842,0.0008869899,0.0008808454,0.001265708,0.002121593,0.001382599,0.003232492,0.0005981977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007033066,"about_ca_system_score_gemma":0.0008542382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002698915,"about_ca_topic_score_gemma":0.003010578,"domain_scores_codex":[0.9986059,0.0006588448,0.00005270368,0.0003302754,0.0002422675,0.000109959],"domain_scores_gemma":[0.9978938,0.001305382,0.0002675679,0.0002246642,0.0002491264,0.00005964255],"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":[0.0001490829,0.00008817412,0.001004424,0.000156736,0.0001094854,0.00008622255,0.0001010579,0.9036599,0.002057473,0.0283431,0.002926279,0.06131804],"study_design_scores_gemma":[0.000003320376,0.0000092987,0.00005184633,0.000003839209,0.000003012355,0.000005317191,0.000003117313,0.9946635,0.0001626906,0.00482479,0.0002661446,0.000003037331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00792547,0.0003796525,0.9903877,0.0002635521,0.00004685224,0.00002091521,0.00007542998,0.0001839697,0.0007164812],"genre_scores_gemma":[0.6199673,0.001010324,0.3672674,0.0006549404,0.0003539317,0.0004488771,0.001068665,0.0003261647,0.008902404],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002747553,"threshold_uncertainty_score":0.0145306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08335694360462259,"score_gpt":0.1725509826350758,"score_spread":0.08919403903045317,"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."}}