{"id":"W3158545781","doi":"10.1109/iccv48922.2021.01198","title":"Vector Neurons: A General Framework for SO(3)-Equivariant Networks","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":205,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Equivariant map; Computer science; Generalization; Pooling; Simplicity; Code (set theory); Artificial neural network; State (computer science); Artificial intelligence; Rotation (mathematics); Simple (philosophy); Theoretical computer science; Limit (mathematics); Segmentation; State vector; Process (computing); Algorithm; Pattern recognition (psychology); Mathematics; Programming language; Pure 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":[0.0005645585,0.001029947,0.0006058479,0.0007789087,0.0003746659,0.001512246,0.002293485,0.001168906,0.004533665],"category_scores_gemma":[0.001415333,0.0004775583,0.001142626,0.0008264785,0.001264862,0.001968313,0.001861011,0.002204473,0.001805184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009130007,"about_ca_system_score_gemma":0.000803304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003701115,"about_ca_topic_score_gemma":0.005183058,"domain_scores_codex":[0.9996712,0.00007614073,0.00001939977,0.00009055354,0.0000949624,0.00004771991],"domain_scores_gemma":[0.9997835,0.00005209542,0.00002804583,0.00005667624,0.00005634212,0.00002335991],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000777329,0.00003399049,0.0006146039,0.0001379115,0.0000799664,0.0001486101,0.0001360137,0.3614647,0.0121086,0.5059147,0.005645953,0.1136372],"study_design_scores_gemma":[0.000006562185,0.0000295441,0.0001155169,0.00001718733,0.00001118175,0.00006246641,0.00001883368,0.8213564,0.002372547,0.168052,0.007942945,0.00001475737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002214282,0.0001267322,0.9948415,0.0001153576,0.00004334653,0.00002135245,0.0001645802,0.0004420341,0.002030788],"genre_scores_gemma":[0.2714207,0.001187466,0.7077063,0.0004479344,0.0001901831,0.0004656997,0.001263421,0.000984431,0.0163337],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004533665,"threshold_uncertainty_score":0.01516664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03456609211711004,"score_gpt":0.2956440832736745,"score_spread":0.2610779911565644,"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."}}