{"id":"W3171439159","doi":"10.1109/access.2021.3086321","title":"Substituting Convolutions for Neural Network Compression","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto","funders":"Staatssekretariat für Bildung, Forschung und Innovation; Engineering and Physical Sciences Research Council; European Commission","keywords":"Computer science; Pointwise; Leverage (statistics); Hyperparameter; Convolutional neural network; Artificial neural network; Network architecture; Data compression ratio; Simple (philosophy); Pareto principle; Computer engineering; Artificial intelligence; Machine learning; Mathematical optimization; Image compression; Mathematics; Computer network","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":[0.0007466687,0.0009252967,0.0005011017,0.0005628349,0.000334341,0.0008761453,0.001380871,0.0009346139,0.006251696],"category_scores_gemma":[0.004487605,0.0002923239,0.0004963776,0.0007802731,0.0009298646,0.002036883,0.001424809,0.001509707,0.002147274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005570087,"about_ca_system_score_gemma":0.0006453918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001146287,"about_ca_topic_score_gemma":0.002335232,"domain_scores_codex":[0.9995838,0.00008430216,0.00002607978,0.00007439136,0.0001720258,0.00005934911],"domain_scores_gemma":[0.999137,0.0003431331,0.00005614846,0.0003156687,0.0001216248,0.00002657276],"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":[0.0003873259,0.0001358048,0.0009459095,0.0002692833,0.00007716641,0.0003266339,0.0001719056,0.2398801,0.03936369,0.155664,0.007520347,0.5552579],"study_design_scores_gemma":[0.00003281625,0.0001261851,0.0003353304,0.00006588652,0.00003069723,0.0002783838,0.00003384213,0.8765067,0.04485386,0.06394971,0.01376328,0.00002326557],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03879979,0.0008493751,0.9477183,0.0004658159,0.0002102219,0.0000769898,0.0001415609,0.00220579,0.009532201],"genre_scores_gemma":[0.4184695,0.001031553,0.5679148,0.0004951029,0.0001739276,0.0002011827,0.0003920872,0.0006223214,0.0106995],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006251696,"threshold_uncertainty_score":0.02091402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06417957861793859,"score_gpt":0.3479631749883805,"score_spread":0.2837835963704419,"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."}}