{"id":"W2770212906","doi":"10.1145/3130800.3130841","title":"Learning to group discrete graphical patterns","year":2017,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Measure (data warehouse); Computer science; Granularity; Feature (linguistics); Artificial intelligence; Element (criminal law); Convolutional neural network; Pattern recognition (psychology); Noise (video); Encoding (memory); Context (archaeology); Key (lock); Deep learning; Group (periodic table); Theoretical computer science; Machine learning; Data mining; Image (mathematics)","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":[0.0006309602,0.0009487447,0.0007720403,0.001579926,0.0004351253,0.001179342,0.001474103,0.00112094,0.004037326],"category_scores_gemma":[0.00372045,0.0005437842,0.001070707,0.001367375,0.001115431,0.002688999,0.001961143,0.001503927,0.001260264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009234208,"about_ca_system_score_gemma":0.0006789371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002400044,"about_ca_topic_score_gemma":0.006281569,"domain_scores_codex":[0.9991443,0.0001412636,0.00004675932,0.0003871545,0.0002007458,0.00007972012],"domain_scores_gemma":[0.9988145,0.0002973367,0.0002188051,0.0004122677,0.0001553109,0.0001017994],"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":[0.0001650051,0.0002448809,0.007500963,0.0004586808,0.0001681533,0.0001667702,0.0004270709,0.2030552,0.0168534,0.06213177,0.01188114,0.6969469],"study_design_scores_gemma":[0.00002229829,0.0001415837,0.001370763,0.00006198969,0.00003397212,0.0001132302,0.00009460786,0.8745077,0.005387124,0.1103369,0.007908262,0.00002158623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03464843,0.0002467017,0.9586356,0.000347532,0.00005378811,0.00008906173,0.000543127,0.00220948,0.003226279],"genre_scores_gemma":[0.4422522,0.0003171132,0.5481349,0.0004102499,0.00006121111,0.0002375206,0.002665111,0.0002898829,0.005631814],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004037326,"threshold_uncertainty_score":0.01350617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01649311156644022,"score_gpt":0.2519915800317465,"score_spread":0.2354984684653063,"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."}}