{"id":"W3034224723","doi":"10.1109/cvprw50498.2020.00142","title":"Generalized Autoencoder for Volumetric Shape Generation","year":2020,"lang":"en","type":"article","venue":"","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Autoencoder; Artificial intelligence; Chamfer (geometry); Computer science; Generative model; Manifold (fluid mechanics); Generative grammar; Function (biology); Pattern recognition (psychology); Space (punctuation); Computer vision; Deep learning; Algorithm; Mathematics; Geometry; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004059921,0.00007565923,0.0001110539,0.00005658513,0.00003737155,0.00003296752,0.00006006806,0.0000380426,0.0003075894],"category_scores_gemma":[0.00002571984,0.00007010411,0.00008287084,0.000261418,0.000002940515,0.00005269457,0.000006276844,0.00003517853,0.0000576771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000128732,"about_ca_system_score_gemma":0.000005458563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005389505,"about_ca_topic_score_gemma":0.000004002818,"domain_scores_codex":[0.9995568,0.000004742045,0.0001310551,0.0001192286,0.00007054547,0.0001176165],"domain_scores_gemma":[0.9998177,0.000009421162,0.000008052611,0.00006573088,0.00003279674,0.00006625774],"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":[0.000001416593,0.000003651497,0.00004233685,0.00001851923,0.00004541079,2.980966e-7,0.00007390494,0.9456139,0.009232517,0.0001645286,0.03157319,0.01323039],"study_design_scores_gemma":[0.0001927319,0.00001212864,0.000008341571,7.033401e-7,0.0000277067,1.548495e-7,0.000006266657,0.9941141,0.001889156,0.00002526979,0.003624188,0.00009922442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05608823,0.000272322,0.9418293,0.0004834772,0.00008119408,0.00006733992,0.00000524357,0.0004195365,0.0007533542],"genre_scores_gemma":[0.9463701,0.00003430659,0.05192392,0.0006632435,0.0004153007,0.00002150774,0.00004255453,0.00002329439,0.0005057761],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8902819,"threshold_uncertainty_score":0.3367888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05279041929718365,"score_gpt":0.2356813011805911,"score_spread":0.1828908818834075,"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."}}