{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006730877,0.0001523003,0.0001517173,0.0001752786,0.0007896076,0.0001653071,0.0004040843,0.00004135143,0.00004033459],"category_scores_gemma":[0.0001605328,0.0001609992,0.00003659288,0.0002170733,0.00002389767,0.0004158739,0.0005707931,0.0005024938,0.000005956229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006281018,"about_ca_system_score_gemma":0.00003821244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000516913,"about_ca_topic_score_gemma":0.00001943968,"domain_scores_codex":[0.9987752,0.0002030147,0.0001852566,0.0004544375,0.0001477849,0.0002343457],"domain_scores_gemma":[0.999245,0.0001918566,0.0001404283,0.0003301563,0.00003127311,0.00006124004],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001207624,0.0003000084,0.01250382,0.0001709892,0.00008233931,0.0000393561,0.00275911,0.0008614262,0.01911658,0.007200488,0.04102267,0.9158224],"study_design_scores_gemma":[0.0004277683,0.0002685089,0.001301389,0.0000173006,0.00001987975,0.0001205866,0.00008703191,0.3185893,0.001333101,0.0008402005,0.6767277,0.0002672813],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03304979,0.001406646,0.9606777,0.0006997928,0.0009778029,0.0004584435,0.0003130836,0.001299409,0.001117375],"genre_scores_gemma":[0.8980947,0.00002359703,0.09956363,0.00009264467,0.0001427367,0.0001237095,0.001230338,0.00003109701,0.000697517],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9155552,"threshold_uncertainty_score":0.6565359,"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."}}