{"id":"W4388739641","doi":"10.2139/ssrn.4634958","title":"S2match: Self-Paced Sampling for Data-Limited Semi-Supervised Learning","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Sampling (signal processing); Computer science; Semi-supervised learning; Artificial intelligence; Machine learning; Statistics; Psychology; Mathematics; Computer vision","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":[0.005171672,0.001335059,0.002343952,0.001108399,0.0008366777,0.001392157,0.005018921,0.00320304,0.006153835],"category_scores_gemma":[0.01822319,0.0009953205,0.001030608,0.001245758,0.001165678,0.002546191,0.004408524,0.003173156,0.002926047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006855214,"about_ca_system_score_gemma":0.001755778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003117414,"about_ca_topic_score_gemma":0.006377564,"domain_scores_codex":[0.9972664,0.001113626,0.000146944,0.0007841899,0.0005376435,0.0001512706],"domain_scores_gemma":[0.9929668,0.004236051,0.0002043475,0.00146065,0.0007149666,0.0004172941],"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":[0.001769435,0.0007970889,0.002797771,0.0004740462,0.0003749883,0.0002573108,0.0003408274,0.1881361,0.01517638,0.01535662,0.02293261,0.7515868],"study_design_scores_gemma":[0.00005404732,0.00007714902,0.0001419767,0.000009234456,0.0000102533,0.00003650857,0.0000141479,0.9894386,0.002342676,0.006662933,0.00120289,0.000009520896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008057544,0.0002595508,0.9849562,0.00009993205,0.0001233283,0.0001490396,0.0002103776,0.005738107,0.0004059007],"genre_scores_gemma":[0.1949002,0.0001648221,0.7954758,0.0005543904,0.0002094496,0.0007184307,0.002524571,0.001639284,0.003813115],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006153835,"threshold_uncertainty_score":0.02735072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09530627498332121,"score_gpt":0.324609153990968,"score_spread":0.2293028790076467,"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."}}