{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006920683,0.0007366834,0.0007433111,0.0004935516,0.0002182997,0.00056144,0.001135284,0.001164077,0.002530314],"category_scores_gemma":[0.001684667,0.0005930515,0.001001993,0.0005268091,0.0008023328,0.0009170941,0.001070971,0.001644871,0.0009351973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007136848,"about_ca_system_score_gemma":0.0006191717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00358418,"about_ca_topic_score_gemma":0.005336438,"domain_scores_codex":[0.999697,0.00006699369,0.00001251702,0.00008359186,0.0001045396,0.00003539205],"domain_scores_gemma":[0.9995465,0.0002267001,0.00003618132,0.00007870348,0.00009072099,0.00002124697],"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":[0.00003510438,0.00002330463,0.0002799517,0.00003310409,0.00002884032,0.00004912637,0.00002883185,0.9079466,0.005370432,0.0120457,0.001079186,0.07307982],"study_design_scores_gemma":[0.000001165806,0.00000481683,0.00003276685,0.000002085255,0.000001506308,0.000009854571,0.000001177054,0.9969362,0.0005852628,0.002174378,0.0002486527,0.000002061299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005760541,0.0001047472,0.9927264,0.00007604378,0.00002202408,0.0000142499,0.00005908266,0.0004593779,0.0007775032],"genre_scores_gemma":[0.5062397,0.0004222571,0.4834341,0.0003937412,0.00006703259,0.0002013051,0.0008498199,0.000361313,0.00803079],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00358418,"threshold_uncertainty_score":0.008464694,"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."}}