{"id":"W3138430199","doi":"10.48550/arxiv.2103.09458","title":"Learning Discriminative Prototypes with Dynamic Time Warping","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Dynamic time warping; Discriminative model; Computer science; Automatic summarization; Artificial intelligence; Pattern recognition (psychology); Image warping; Segmentation; Machine learning; Speech recognition","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":[0.0008333065,0.001604681,0.001376172,0.001484033,0.000446145,0.00114122,0.001677747,0.00145094,0.002544461],"category_scores_gemma":[0.004899879,0.000808265,0.001054267,0.001963348,0.0007395969,0.002437068,0.001399025,0.002176843,0.001969856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005033116,"about_ca_system_score_gemma":0.0008507606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00315413,"about_ca_topic_score_gemma":0.003308799,"domain_scores_codex":[0.9991663,0.000120322,0.00004802031,0.0004238883,0.0001551216,0.00008648678],"domain_scores_gemma":[0.998976,0.0003554755,0.0001401516,0.0002391799,0.000207694,0.00008154421],"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":[0.0003901327,0.0001815199,0.001771383,0.0002293765,0.0001189989,0.0002439624,0.0001698375,0.1375456,0.02684898,0.009420351,0.01210408,0.8109757],"study_design_scores_gemma":[0.00001183334,0.00006793777,0.0003520398,0.00001673496,0.00001271388,0.0000824407,0.00003460203,0.9824947,0.005399792,0.00957936,0.001930392,0.00001752986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02078911,0.0004949427,0.9757115,0.0001344004,0.0001145194,0.00005755604,0.0002992903,0.001584453,0.0008142715],"genre_scores_gemma":[0.4511701,0.0009077009,0.5379369,0.0002645788,0.0001814853,0.000352184,0.00385149,0.0005845753,0.00475105],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00315413,"threshold_uncertainty_score":0.00851208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03001365521484318,"score_gpt":0.1649493341838101,"score_spread":0.1349356789689669,"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."}}