{"id":"W4382457524","doi":"10.1609/aaai.v37i1.25150","title":"RankDNN: Learning to Rank for Few-Shot Learning","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Science and Technology Commission of Shanghai Municipality; Fudan University","keywords":"Computer science; Ranking (information retrieval); Artificial intelligence; Learning to rank; Ranking SVM; Artificial neural network; Pipeline (software); Pattern recognition (psychology); Benchmark (surveying); Feature (linguistics); Machine learning; Rank (graph theory); Margin (machine learning); Domain (mathematical analysis); Similarity (geometry); Deep learning; Image (mathematics); Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001511608,0.0002679039,0.0003393956,0.000348308,0.0006421217,0.0004716396,0.001888699,0.0001034064,0.00006807905],"category_scores_gemma":[0.002492967,0.0002255563,0.0001976379,0.001834206,0.0001269017,0.0004021778,0.0004640236,0.0005392949,0.0005868446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004830959,"about_ca_system_score_gemma":0.00009690475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001479424,"about_ca_topic_score_gemma":0.000003878341,"domain_scores_codex":[0.9974555,0.00004547195,0.0005955362,0.0006726963,0.0006190318,0.0006117097],"domain_scores_gemma":[0.9981008,0.0003845826,0.0003628379,0.0002406773,0.0007377261,0.0001734345],"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":[0.0001528935,0.0000560817,0.0002831713,0.00006617404,0.00002402903,8.666367e-7,0.008206062,0.01104715,0.07060134,0.7111844,0.0006356465,0.1977421],"study_design_scores_gemma":[0.0001633786,0.0007982929,0.0005511549,0.000389079,0.00002129849,0.000005568578,0.005986984,0.6864759,0.2209691,0.0658898,0.01807705,0.000672425],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2591648,0.00004475301,0.6560024,0.0279188,0.002360967,0.002951746,0.000006628512,0.002072439,0.04947749],"genre_scores_gemma":[0.9896939,0.00003019037,0.005714618,0.0003826982,0.0001086441,0.0001095771,0.000001713916,0.00002888234,0.00392982],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7305291,"threshold_uncertainty_score":0.9197918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1333800895186134,"score_gpt":0.3341693147696342,"score_spread":0.2007892252510208,"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."}}