{"id":"W4400680585","doi":"10.1109/ispass61541.2024.00044","title":"Distributed Training of Neural Radiance Fields: A Performance Characterization","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Radiance; Training (meteorology); Computer science; Characterization (materials science); Artificial neural network; Artificial intelligence; Remote sensing; Geology; Optics; Meteorology","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":[0.001095651,0.0007972411,0.0006341792,0.0004315317,0.0003767449,0.0007439435,0.001464379,0.0009445983,0.002956221],"category_scores_gemma":[0.005786394,0.0002781898,0.0003110645,0.0007501595,0.0005352316,0.001974473,0.001197772,0.0009843619,0.0006884004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001125784,"about_ca_system_score_gemma":0.001180123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005955262,"about_ca_topic_score_gemma":0.006193639,"domain_scores_codex":[0.9993536,0.00008512681,0.00003622664,0.0002142871,0.0001942214,0.0001166263],"domain_scores_gemma":[0.9974981,0.001223573,0.0001488272,0.0006761085,0.0003385768,0.0001147391],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001527956,0.0003776827,0.006117879,0.0003049808,0.00009865152,0.0002061039,0.0001927745,0.62234,0.03078801,0.006493666,0.006286636,0.3252656],"study_design_scores_gemma":[0.00004299664,0.0001390108,0.001078876,0.00001038452,0.00001178921,0.0000729781,0.00003926633,0.9834796,0.01228334,0.001851308,0.0009829272,0.000007600272],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5029477,0.00225212,0.4686675,0.001351559,0.0001546831,0.000194579,0.0006022052,0.007917794,0.01591182],"genre_scores_gemma":[0.9110631,0.0004976057,0.08447665,0.0001317568,0.00004713919,0.0001244927,0.0008028234,0.0003347542,0.002521767],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005955262,"threshold_uncertainty_score":0.01184118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02328557826862566,"score_gpt":0.2355153192751852,"score_spread":0.2122297410065596,"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."}}