{"id":"W2991222810","doi":"10.48550/arxiv.1911.12247","title":"Contrastive Learning of Structured World Models","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute for Catastrophic Loss Reduction","keywords":"Computer science; Representation (politics); Object (grammar); Embedding; Set (abstract data type); Artificial intelligence; Process (computing); Graph; Class (philosophy); Theoretical computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002334666,0.0002646465,0.0004175529,0.000405193,0.00009872918,0.0000828703,0.001268976,0.0001815432,0.00005593664],"category_scores_gemma":[0.00004310091,0.0003096701,0.0002030877,0.0005945824,0.00009554834,0.0004174044,0.001103938,0.0008929528,0.00004464129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001149187,"about_ca_system_score_gemma":0.0002155131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006715878,"about_ca_topic_score_gemma":0.00002122811,"domain_scores_codex":[0.9982929,0.0002214023,0.000232082,0.0008259296,0.0001337192,0.0002939738],"domain_scores_gemma":[0.9982938,0.0001939798,0.0004832328,0.0006792386,0.0002293552,0.0001203879],"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":[0.00002099109,0.00001389205,0.0008971169,0.00003371414,0.00005541904,0.00002485914,0.0004665545,0.7036691,0.000044586,0.2941211,0.0000311449,0.0006215542],"study_design_scores_gemma":[0.0005303241,0.00003522207,0.001363856,0.0000945478,0.00003123112,0.000001197965,0.0001704654,0.9533468,0.00009593365,0.04357158,0.0004557758,0.0003030561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04797205,0.00006355742,0.9334519,0.00003049707,0.0004787975,0.0002333271,0.000005843305,0.0001734206,0.01759062],"genre_scores_gemma":[0.988448,0.00004150817,0.003837526,0.00005445928,0.00002937613,3.469904e-7,0.00001230714,0.00001693067,0.00755953],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9404759,"threshold_uncertainty_score":0.9999356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07245122322623201,"score_gpt":0.1855727580677684,"score_spread":0.1131215348415364,"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."}}