{"id":"W3184489883","doi":"10.1109/cvpr52688.2022.00566","title":"Parametric Scattering Networks","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; University of Waterloo; Université de Montréal; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wavelet transform; Wavelet; Scattering; Filter bank; Parametric statistics; Discriminative model; Morlet wavelet; Computer science; Filter (signal processing); Mathematics; Wavelet packet decomposition; Artificial intelligence; Discrete wavelet transform; Pattern recognition (psychology); Computer vision; Statistics; Physics; Optics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009991742,0.001350909,0.0008620124,0.001557165,0.0006158187,0.001531812,0.001580966,0.001729184,0.007273319],"category_scores_gemma":[0.005571391,0.0006296341,0.001151017,0.001302344,0.001049208,0.002443037,0.001795072,0.001659228,0.002245803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001012234,"about_ca_system_score_gemma":0.0007016641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002262987,"about_ca_topic_score_gemma":0.003339606,"domain_scores_codex":[0.9993535,0.0001926541,0.00002704662,0.0002258189,0.0001327672,0.00006834047],"domain_scores_gemma":[0.9982302,0.0009643994,0.0001797563,0.0002596464,0.0002857753,0.00008014733],"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":[0.0001443674,0.00008008053,0.002229256,0.000140446,0.0001212568,0.0001647373,0.0001206677,0.6967173,0.00310653,0.08498387,0.008329706,0.2038618],"study_design_scores_gemma":[0.00000537213,0.00001883819,0.0002073801,0.00001616404,0.00001272045,0.00005670528,0.00001974799,0.9506742,0.0005452204,0.04627074,0.00216434,0.000008472821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02670816,0.0007861559,0.9598701,0.0005898726,0.0001053642,0.00008208268,0.0007472641,0.0007153418,0.01039573],"genre_scores_gemma":[0.7702689,0.002093779,0.1971823,0.0005053591,0.0003437152,0.0003841803,0.004530431,0.0003480312,0.02434336],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007273319,"threshold_uncertainty_score":0.02433169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03890684951204379,"score_gpt":0.2652592332562957,"score_spread":0.2263523837442519,"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."}}