{"id":"W3214266825","doi":"","title":"ATISS: Autoregressive Transformers for Indoor Scene Synthesis","year":2021,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Autoregressive model; Transformer; Texture synthesis; Minimum bounding box; Permutation (music); Floor plan; Architecture; Artificial intelligence; Embedding; Computer vision; Algorithm; Image (mathematics); Engineering drawing; Image processing; Engineering; 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":[0.0006229856,0.001282001,0.0006322923,0.0007458357,0.0002813168,0.0009371328,0.001862118,0.000912929,0.0073217],"category_scores_gemma":[0.0016853,0.0008150922,0.001852939,0.0006799411,0.0006870123,0.00106991,0.001284586,0.002005768,0.00466809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008749027,"about_ca_system_score_gemma":0.0008451605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006304852,"about_ca_topic_score_gemma":0.01483233,"domain_scores_codex":[0.9996158,0.00007861389,0.00001503243,0.0001386747,0.0001101082,0.00004176204],"domain_scores_gemma":[0.9996858,0.0001509015,0.00002334416,0.00007206208,0.00004219397,0.000025748],"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":[0.0002717771,0.0001177766,0.0008497083,0.0002108761,0.0001328876,0.0001760093,0.0001186917,0.6444792,0.02340785,0.0180433,0.01768279,0.2945091],"study_design_scores_gemma":[0.0000170829,0.0000307277,0.0001006127,0.000008237022,0.00001026321,0.00005006436,0.000009349672,0.9843138,0.004226663,0.006800566,0.00442181,0.00001081611],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003544505,0.0001993988,0.9830751,0.0000729526,0.00005450673,0.00005003999,0.0005996315,0.01067619,0.001727648],"genre_scores_gemma":[0.3045195,0.0005286074,0.6775787,0.0003258925,0.00008920773,0.0003384066,0.006186507,0.002669276,0.007763807],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0073217,"threshold_uncertainty_score":0.02449346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02360552716175293,"score_gpt":0.2305080485321016,"score_spread":0.2069025213703486,"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."}}