{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004375378,0.0002611497,0.0002667275,0.0002210339,0.0003922599,0.0006959611,0.0007017183,0.0001209169,0.0004496034],"category_scores_gemma":[0.0009282581,0.0002730931,0.0001907674,0.0003604737,0.00004870323,0.0006257375,0.0001236486,0.0009906272,0.0000776038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009667384,"about_ca_system_score_gemma":0.0001423576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002901633,"about_ca_topic_score_gemma":0.00003096711,"domain_scores_codex":[0.997538,0.0004017092,0.0003206499,0.0007755893,0.0006226345,0.0003413833],"domain_scores_gemma":[0.998162,0.0005069523,0.0002634099,0.0002699716,0.0006753714,0.0001222595],"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":[0.0003295909,0.0002439428,0.006773518,0.00004421261,0.0003166615,0.0001334545,0.00926083,0.1070747,0.02490452,0.6258209,0.0004604141,0.2246372],"study_design_scores_gemma":[0.001533819,0.0002643754,0.002621025,0.0001099317,0.00001811547,0.00004646367,0.001802524,0.9289253,0.004342144,0.006324042,0.05353519,0.0004770671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01432569,0.0001146324,0.923628,0.01513544,0.00116662,0.0002905365,0.00001248722,0.0003985701,0.04492798],"genre_scores_gemma":[0.955965,0.00006666579,0.03048372,0.0006378486,0.0001924391,0.00006617625,0.0002798008,0.00002991449,0.01227839],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9416394,"threshold_uncertainty_score":0.9999721,"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."}}