{"id":"W3009482281","doi":"10.1109/icra40945.2020.9197362","title":"DeepMEL: Compiling Visual Multi-Experience Localization into a Deep Neural Network","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Leverage (statistics); Visual odometry; Computer science; Path (computing); Artificial intelligence; Odometry; Artificial neural network; Deep neural networks; Computer vision; Robot; Range (aeronautics); Mobile robot; Engineering","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":[0.000326185,0.001270578,0.0005046363,0.0004594697,0.0002813828,0.000726644,0.001964707,0.001014621,0.006895363],"category_scores_gemma":[0.001296663,0.0006209269,0.0006000509,0.0003842646,0.0004283113,0.001555791,0.001542469,0.001870243,0.002863239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009252976,"about_ca_system_score_gemma":0.0009179898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0109196,"about_ca_topic_score_gemma":0.02144265,"domain_scores_codex":[0.9998327,0.00001325262,0.000006087036,0.00008306811,0.00003391115,0.00003081844],"domain_scores_gemma":[0.9997326,0.00007544764,0.0000264738,0.00006720224,0.00006840938,0.00002987447],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002124809,0.0002681018,0.002332259,0.0001930853,0.0001773257,0.0001677293,0.0001019239,0.4058163,0.01798743,0.004766062,0.0191533,0.5488241],"study_design_scores_gemma":[0.00001317863,0.00006443882,0.0002661619,0.00001249194,0.00001188156,0.00002108362,0.0000134186,0.9897792,0.004473198,0.003527024,0.001808589,0.000009368264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02364343,0.000333528,0.9396589,0.0002903847,0.0001772537,0.00008828338,0.0008659296,0.03141281,0.00352948],"genre_scores_gemma":[0.522106,0.0003274697,0.4589921,0.0005088332,0.00007633116,0.0002999243,0.003873111,0.00114881,0.0126674],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0109196,"threshold_uncertainty_score":0.0230673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02549346913501806,"score_gpt":0.2729217937731298,"score_spread":0.2474283246381117,"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."}}