{"id":"W4388775456","doi":"10.1038/s42256-023-00743-0","title":"Differentiable visual computing for inverse problems and machine learning","year":2023,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Differentiable function; Graphics pipeline; Computer science; Graphics; Computer graphics; Artificial intelligence; Pipeline (software); Robotics; Physics engine; Visualization; Geometric primitive; Human–computer interaction; Computer graphics (images); 3D computer graphics; Robot; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005723105,0.0002207409,0.0002217012,0.0003894328,0.0003337312,0.0002905234,0.0006364022,0.0001758997,0.000005974076],"category_scores_gemma":[0.0001340067,0.0001988616,0.00008143938,0.001138065,0.00004843888,0.0002226365,0.0006839963,0.0005978963,0.000007863225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001700111,"about_ca_system_score_gemma":0.0000197107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004975445,"about_ca_topic_score_gemma":0.00003349052,"domain_scores_codex":[0.9984711,0.00006718958,0.000303036,0.0005566598,0.0002512955,0.0003507549],"domain_scores_gemma":[0.9990704,0.0002976989,0.0001289929,0.0002471307,0.0001535254,0.0001022335],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001575586,0.0001340108,0.02388459,0.0003480716,0.00006621751,0.00001291568,0.001932243,0.00190385,0.0006648872,0.794286,0.001424321,0.1753272],"study_design_scores_gemma":[0.0000962416,0.0001722777,0.0007607405,0.0000591696,0.000005221573,0.000009713356,0.00000955587,0.9619549,0.004902902,0.02486951,0.006929311,0.0002304661],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01765347,0.000643001,0.9795402,0.0003065,0.0002802477,0.0003677622,0.000005313477,0.001129447,0.00007405092],"genre_scores_gemma":[0.9808274,0.0003085481,0.01805812,0.0003461765,0.00008092182,0.0000215908,0.00005286734,0.00002439514,0.0002799807],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9631739,"threshold_uncertainty_score":0.8109342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02425735947625895,"score_gpt":0.316345542072652,"score_spread":0.292088182596393,"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."}}