{"id":"W2102630415","doi":"10.1145/1015330.1015371","title":"Generative modeling for continuous non-linearly embedded visual inference","year":2004,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":155,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Inference; Computer science; Representation (politics); Generative model; Artificial intelligence; Interpolation (computer graphics); Curse of dimensionality; Dimensionality reduction; Motion (physics); Machine learning; Computer vision; Generative grammar; Algorithm","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":[0.0001001206,0.0001201263,0.0001481225,0.00005950266,0.0001397346,0.0001771812,0.0003504177,0.0000299474,0.000006242816],"category_scores_gemma":[0.00007034583,0.000100736,0.00005386008,0.0001564366,0.0000188307,0.0007609008,0.0001279741,0.00007974475,0.00003315274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003059833,"about_ca_system_score_gemma":0.00009708071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000186504,"about_ca_topic_score_gemma":0.00000481091,"domain_scores_codex":[0.999069,0.000008519497,0.0001934198,0.0003322264,0.0001383133,0.000258502],"domain_scores_gemma":[0.9994187,0.00004468689,0.00004262225,0.0002149791,0.0001945688,0.00008447442],"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":[0.00003280828,0.0002724486,0.00005614235,0.00001941468,0.00003145693,0.00001639613,0.004806611,0.4623006,0.04387355,0.1919016,0.0002527907,0.2964361],"study_design_scores_gemma":[0.0007501259,0.0001089177,0.000005322894,0.00001529472,0.000001481613,0.000002655564,0.00009221325,0.9693657,0.01552811,0.01386147,0.0001154569,0.0001532313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01003041,0.00002318681,0.9879415,0.000542881,0.0001830948,0.0002039145,8.036641e-7,0.0001578761,0.000916338],"genre_scores_gemma":[0.4902777,0.000002938089,0.5086767,0.0007724557,0.00004480061,0.00001371207,0.000001229881,0.000005114028,0.0002053167],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5070651,"threshold_uncertainty_score":0.4107893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02693962315975816,"score_gpt":0.3465508942465489,"score_spread":0.3196112710867907,"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."}}