{"id":"W3172691730","doi":"","title":"Parameterless Transductive Feature Re-representation for Few-Shot Learning","year":2021,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Feature (linguistics); Computer science; Artificial intelligence; Shot (pellet); Representation (politics); Pattern recognition (psychology); Feature learning; Machine learning; Chemistry","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":[0.001604598,0.001389036,0.002529582,0.001131257,0.00067238,0.001233096,0.003353626,0.002578997,0.004649396],"category_scores_gemma":[0.00661454,0.000584376,0.00117867,0.001390342,0.001041488,0.003833906,0.002752151,0.003427681,0.002668852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009091101,"about_ca_system_score_gemma":0.001057683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003352226,"about_ca_topic_score_gemma":0.003765006,"domain_scores_codex":[0.9985974,0.0003868306,0.00008092017,0.0005069583,0.0002775675,0.000150188],"domain_scores_gemma":[0.9975615,0.0009536828,0.0001180116,0.0008772309,0.0003879128,0.0001017394],"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":[0.0005020482,0.0005262675,0.0009151775,0.0002888222,0.0001575089,0.0001654896,0.0001657767,0.1578477,0.01719886,0.01486385,0.01619934,0.7911691],"study_design_scores_gemma":[0.00001353432,0.00009158743,0.0002121744,0.00001773542,0.0000182347,0.00007067798,0.00002812607,0.9751452,0.003746693,0.01929291,0.001342089,0.00002110267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01004345,0.0006446638,0.9859371,0.0002004257,0.0001099808,0.0000764798,0.0002719244,0.002002808,0.0007131768],"genre_scores_gemma":[0.5852768,0.0008136289,0.3954784,0.0008640138,0.0002969177,0.000564555,0.004666066,0.0007283285,0.01131116],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004649396,"threshold_uncertainty_score":0.01555377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09124599111745589,"score_gpt":0.3547058943733342,"score_spread":0.2634599032558783,"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."}}