{"id":"W3116263883","doi":"10.1109/iccv48922.2021.01533","title":"Prediction by Anticipation: An Action-Conditional Prediction Method based on Interaction Learning","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Huawei Technologies (Canada)","funders":"","keywords":"Anticipation (artificial intelligence); Generalization; Probabilistic logic; Computer science; Generative model; Generative grammar; Artificial intelligence; Machine learning; Action (physics); Bayesian probability; Range (aeronautics); Conditional probability distribution; Process (computing); Pedestrian; Conditional probability; Mathematics; Engineering; Econometrics; Statistics","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":[0.001223386,0.001303228,0.001592553,0.0009030673,0.0004636761,0.0006773294,0.003163468,0.001313833,0.002260043],"category_scores_gemma":[0.002934949,0.0007896011,0.001229049,0.001118997,0.0007145836,0.001752628,0.001634084,0.003295477,0.000796116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007728813,"about_ca_system_score_gemma":0.001537243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01767665,"about_ca_topic_score_gemma":0.02187293,"domain_scores_codex":[0.9992201,0.000170678,0.00003249758,0.0002958274,0.0001786994,0.0001021375],"domain_scores_gemma":[0.9985038,0.0009324468,0.0001255865,0.0001413794,0.0002007392,0.00009617105],"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":[0.0005569782,0.000505034,0.00783216,0.0001368599,0.0001945535,0.0002462075,0.0002494226,0.5630364,0.005407206,0.009597453,0.01036083,0.4018769],"study_design_scores_gemma":[0.000007809157,0.00002830946,0.0002427266,0.000005182911,0.000008900147,0.00001907355,0.000005414483,0.9961521,0.000325768,0.002862574,0.0003352964,0.000006859997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0280963,0.0006759311,0.966139,0.000444813,0.00008533408,0.00006887688,0.0004327077,0.002646146,0.001410946],"genre_scores_gemma":[0.754135,0.0005769792,0.2354357,0.0006345073,0.0002241935,0.0002370323,0.002953801,0.0004132545,0.005389528],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01767665,"threshold_uncertainty_score":0.03514755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04204573715458546,"score_gpt":0.3237950629773097,"score_spread":0.2817493258227243,"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."}}