{"id":"W2952769376","doi":"10.48550/arxiv.1806.07011","title":"VirtualHome: Simulating Household Activities via Programs","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Defense Advanced Research Projects Agency; Intelligence Advanced Research Projects Activity; Samsung; Nvidia","keywords":"Computer science; Task (project management); Variety (cybernetics); Representation (politics); Human–computer interaction; Interface (matter); Code (set theory); Game engine; Artificial intelligence; Programming language","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":[0.0002221752,0.0007116463,0.0002695321,0.0003773669,0.0001940775,0.0005582976,0.001205242,0.0006788672,0.003382182],"category_scores_gemma":[0.001262137,0.0002615275,0.0005212859,0.0002798359,0.0006040584,0.0008358147,0.0008710161,0.0005989849,0.0004565223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004684998,"about_ca_system_score_gemma":0.0004010217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008723053,"about_ca_topic_score_gemma":0.01228357,"domain_scores_codex":[0.9998242,0.00006940396,0.000008035065,0.00005224601,0.00002691085,0.00001922247],"domain_scores_gemma":[0.9997221,0.0001730176,0.00001933122,0.00003702504,0.00002100258,0.00002760493],"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":[0.000546777,0.0004374281,0.008295019,0.0004307111,0.00008616341,0.0005366736,0.001032428,0.8956008,0.01358184,0.01202983,0.00888815,0.05853415],"study_design_scores_gemma":[0.00004249073,0.00008388136,0.001359823,0.00002275306,0.00001270344,0.00008067508,0.000164585,0.9821862,0.00532971,0.004042203,0.006658094,0.00001683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4229112,0.0003022194,0.5511867,0.0003572113,0.00009070214,0.0005008319,0.005214152,0.01057445,0.008862592],"genre_scores_gemma":[0.8103216,0.000226662,0.1798459,0.0001185033,0.00001111359,0.0005235292,0.004983422,0.0005584905,0.003410816],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008723053,"threshold_uncertainty_score":0.01734453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0827059822227267,"score_gpt":0.21093085153612,"score_spread":0.1282248693133933,"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."}}