{"id":"W1886634424","doi":"10.48550/arxiv.1206.6445","title":"Deep Lambertian Networks","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Albedo (alchemy); Artificial intelligence; Computer science; Invariant (physics); Generative model; Computer vision; Prior probability; Surface (topology); Latent variable; Representation (politics); Photometric stereo; Object (grammar); Pattern recognition (psychology); Image (mathematics); Generative grammar; Mathematics; Geometry; Bayesian probability","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002808413,0.0003973683,0.0003812435,0.000138392,0.0002194273,0.0001958339,0.001901204,0.0003833022,0.0001051456],"category_scores_gemma":[0.00002280632,0.0004348955,0.0002946706,0.0004821008,0.0001037546,0.0005934837,0.002502847,0.0006414374,0.0001646355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001430392,"about_ca_system_score_gemma":0.00008287287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000141506,"about_ca_topic_score_gemma":0.00009078442,"domain_scores_codex":[0.9977999,0.0002569847,0.0002048454,0.001015625,0.00008149434,0.0006410937],"domain_scores_gemma":[0.9977707,0.0001075022,0.0002347795,0.00144334,0.0001399247,0.0003037442],"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":[0.000009190114,0.00005904894,0.0009286041,0.00001241334,0.00009644553,0.00008251058,0.0001222722,0.9629233,0.000006688498,0.03172954,0.0008271621,0.00320281],"study_design_scores_gemma":[0.0001912601,0.00001603867,0.0008963757,0.00003291507,0.00007326081,0.000003233414,0.00001855929,0.9910144,0.00005168531,0.005499255,0.001706179,0.0004968563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00206682,0.0004429754,0.9894802,0.0001292487,0.001832563,0.0002183901,0.000002383252,0.0002302914,0.00559715],"genre_scores_gemma":[0.9911591,0.0002447861,0.006452858,0.000211795,0.0006010691,0.000001143903,0.00001350168,0.00002449958,0.001291236],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9890923,"threshold_uncertainty_score":0.9998103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05038077141648527,"score_gpt":0.171328178623764,"score_spread":0.1209474072072787,"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."}}