{"id":"W2140045824","doi":"","title":"High Order Regularization for Semi-Supervised Learning of Structured Output Problems","year":2014,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; University of Toronto","funders":"","keywords":"Computer science; Regularization (linguistics); Discriminative model; Machine learning; Artificial intelligence; Labeled data; Semi-supervised learning; Margin (machine learning); Graph; Structured prediction; Segmentation; Pattern recognition (psychology); Theoretical computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003871064,0.0001030277,0.0001594031,0.00009311739,0.0001210754,0.00008784262,0.0003335013,0.0000671602,0.00004137821],"category_scores_gemma":[0.0002685624,0.00009037111,0.00004546096,0.0003257679,0.00002390294,0.0002684903,0.00006692287,0.00009115026,0.000007462184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000123119,"about_ca_system_score_gemma":0.00003430106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001463747,"about_ca_topic_score_gemma":0.000003890381,"domain_scores_codex":[0.9989914,0.00009325789,0.0002529595,0.0002673759,0.0002129572,0.0001820968],"domain_scores_gemma":[0.9991708,0.0001173007,0.0001433047,0.0002650823,0.0002491171,0.000054436],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001000714,0.00002775487,0.000667899,0.00008042426,0.00002358572,1.438606e-7,0.001957894,0.1553899,0.01548178,0.7051963,0.0003162732,0.1208481],"study_design_scores_gemma":[0.0008315163,0.0001310086,0.001429311,0.00001653311,0.000005284495,0.000001192434,0.00006690286,0.9643669,0.003376145,0.01079174,0.01883601,0.0001474191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004217864,0.000009620608,0.9922648,0.0004317863,0.0001832174,0.0002334733,4.285224e-7,0.0001786598,0.002480166],"genre_scores_gemma":[0.7011106,0.000001472434,0.2936331,0.0001779051,0.00003833729,0.0000122165,0.00001849667,0.00001083912,0.004997042],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8089771,"threshold_uncertainty_score":0.3685227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01463017615195387,"score_gpt":0.2216546490827776,"score_spread":0.2070244729308237,"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."}}