{"id":"W3170992784","doi":"10.48550/arxiv.2106.09373","title":"Unsupervised Path Representation Learning with Curriculum Negative Sampling","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"Innovationsfonden; Villum Fonden","keywords":"Path (computing); Computer science; Representation (politics); Artificial intelligence; ENCODE; Machine learning; Feature learning; Unsupervised learning; Ranking (information retrieval)","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":[0.000873294,0.001195014,0.001037163,0.001111651,0.0005892815,0.0006302636,0.002111752,0.001260602,0.002352729],"category_scores_gemma":[0.00444311,0.0005782213,0.0009830793,0.001305788,0.001075701,0.002028799,0.001411208,0.00231435,0.0006930471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009733231,"about_ca_system_score_gemma":0.001247719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005452374,"about_ca_topic_score_gemma":0.00996237,"domain_scores_codex":[0.9994044,0.0001873526,0.00002108901,0.00022352,0.00009450779,0.00006912907],"domain_scores_gemma":[0.9981853,0.0009966708,0.000154712,0.0003094132,0.0002812074,0.00007268652],"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":[0.0002610667,0.0003233656,0.004073776,0.0001620813,0.00009901335,0.0001192531,0.0001470514,0.6139771,0.005841005,0.01849251,0.008005449,0.3484984],"study_design_scores_gemma":[0.00001276386,0.00003403046,0.0002543984,0.000006131809,0.000005907818,0.0000144827,0.00001077717,0.9896185,0.0009744918,0.008574362,0.000488073,0.000006041877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03689512,0.0001701066,0.9595721,0.0001785325,0.00003798212,0.00009326345,0.0002934736,0.001553708,0.001205672],"genre_scores_gemma":[0.6532899,0.0002229418,0.3360547,0.000348427,0.0001092138,0.0004996058,0.003409449,0.0002959669,0.005769775],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005452374,"threshold_uncertainty_score":0.01084125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04621553180203722,"score_gpt":0.1826511330023704,"score_spread":0.1364356012003332,"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."}}