{"id":"W1577845740","doi":"10.1109/dmcc.1991.633203","title":"Linear Speedup of Winograd's Matrix Multiplication Algorithm Using an Array Processor","year":2005,"lang":"en","type":"article","venue":"","topic":"Interconnection Networks and Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Speedup; Computer science; Matrix multiplication; Parallel computing; Multiplication (music); Algorithm; Arithmetic; Mathematics; Combinatorics; Physics","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":[0.0004029839,0.0008309535,0.0005650939,0.0005986557,0.0006840572,0.0008249992,0.001011448,0.0004681448,0.01837202],"category_scores_gemma":[0.001225048,0.0002993876,0.0003808099,0.001302326,0.0003107912,0.001530929,0.0006740351,0.0006470448,0.0031687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007327983,"about_ca_system_score_gemma":0.001347739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01223865,"about_ca_topic_score_gemma":0.01841425,"domain_scores_codex":[0.9996144,0.00008373447,0.00001705657,0.00006854136,0.0001228522,0.00009339509],"domain_scores_gemma":[0.9995511,0.0001594307,0.00001427408,0.00007478384,0.0001609859,0.00003935213],"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":[0.002938441,0.000321164,0.002358032,0.0004277389,0.0001597383,0.0004430723,0.0003345484,0.140403,0.04568609,0.0414561,0.05578095,0.709691],"study_design_scores_gemma":[0.0003360492,0.0002557934,0.0009856136,0.00001848126,0.0000600734,0.0001696862,0.0001010018,0.942161,0.03146994,0.007644309,0.01676733,0.00003065889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3833524,0.001817738,0.5272935,0.0010849,0.001047047,0.0001533554,0.00065731,0.01778118,0.06681257],"genre_scores_gemma":[0.5055479,0.0004963782,0.4623927,0.000147898,0.0001673618,0.0001129219,0.000872176,0.0007362376,0.02952636],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01837202,"threshold_uncertainty_score":0.06146055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03315127482240068,"score_gpt":0.3087668065772507,"score_spread":0.27561553175485,"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."}}