{"id":"W4307765015","doi":"10.3390/make4040048","title":"Lottery Ticket Structured Node Pruning for Tabular Datasets","year":2022,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Pruning; Computer science; Inference; Reduction (mathematics); Range (aeronautics); Ticket; Artificial neural network; Node (physics); Iterative method; Machine learning; Artificial intelligence; Data mining; Algorithm; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003801591,0.0009946887,0.001191046,0.001393479,0.001012656,0.001316142,0.002200985,0.001000971,0.001698918],"category_scores_gemma":[0.01291452,0.000403495,0.001003281,0.001724967,0.0005950221,0.002692529,0.001118291,0.001662239,0.0006481088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008354743,"about_ca_system_score_gemma":0.00108502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005268316,"about_ca_topic_score_gemma":0.01338445,"domain_scores_codex":[0.9985476,0.0004717238,0.0001409806,0.0003537224,0.0003359582,0.0001500739],"domain_scores_gemma":[0.9923342,0.004378277,0.0003529914,0.001659695,0.001086418,0.0001883333],"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":[0.001020192,0.000612855,0.01568915,0.0005720713,0.0005131206,0.0002936702,0.0005885607,0.4704064,0.01148298,0.005586072,0.01261298,0.4806221],"study_design_scores_gemma":[0.00004182494,0.0003426613,0.002080022,0.00005337099,0.00006059676,0.0001342269,0.0001556364,0.979451,0.009332082,0.005037429,0.003282571,0.00002857449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4372236,0.00181466,0.5424855,0.0005111304,0.000364927,0.0004381886,0.001977701,0.01031781,0.004866422],"genre_scores_gemma":[0.5457704,0.0004787526,0.4440085,0.0003160356,0.00005725381,0.000322378,0.005582295,0.0007206968,0.002743656],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005268316,"threshold_uncertainty_score":0.02010494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01451395610005096,"score_gpt":0.3000824495078468,"score_spread":0.2855684934077959,"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."}}