{"id":"W2976040440","doi":"10.48550/arxiv.1909.13165","title":"Relational Graph Learning for Crowd Navigation","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crowds; ENCODE; Reinforcement learning; Computer science; Graph; Artificial intelligence; Representation (politics); Machine learning; Feature learning; Convolutional neural network; Baseline (sea); Exploit; Theoretical computer science; Computer security","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006662666,0.001009749,0.0008142992,0.001251629,0.000594272,0.0009390979,0.001728651,0.001190517,0.002896415],"category_scores_gemma":[0.003625556,0.0004888428,0.0008695936,0.001061924,0.0009276898,0.002549011,0.001865105,0.001700106,0.0008224146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001568257,"about_ca_system_score_gemma":0.001437691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01708288,"about_ca_topic_score_gemma":0.01906948,"domain_scores_codex":[0.99943,0.0001501431,0.00002000389,0.0002169118,0.0001271706,0.00005583339],"domain_scores_gemma":[0.9988868,0.0005178798,0.0001260491,0.000233865,0.0001562443,0.00007915888],"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":[0.00009666761,0.00008453141,0.001355578,0.0001000291,0.00007666286,0.00011337,0.000152541,0.8172699,0.002607234,0.05635935,0.00534727,0.1164369],"study_design_scores_gemma":[0.000004278001,0.00001156356,0.00009203417,0.000005043256,0.000006388687,0.00001259729,0.00001241475,0.9536878,0.0005714709,0.04452921,0.001061642,0.000005674461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007567825,0.0002177602,0.9890329,0.0002529361,0.0000324704,0.00002478618,0.0001966094,0.001431823,0.001242864],"genre_scores_gemma":[0.6130246,0.0005645839,0.3787043,0.0003678294,0.00008255371,0.0001370425,0.001625883,0.0004478821,0.005045271],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01708288,"threshold_uncertainty_score":0.0339669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0738761143946521,"score_gpt":0.1995994911295,"score_spread":0.125723376734848,"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."}}