{"id":"W4413158559","doi":"10.1109/cvpr52734.2025.02201","title":"ReNeg: Learning Negative Embedding with Reward Guidance","year":2025,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"National Natural Science Foundation of China","keywords":"Computer science; Embedding; Artificial intelligence","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":[0.001372506,0.00203519,0.0009683675,0.0005135224,0.0003413853,0.0008774971,0.002218976,0.001496612,0.005719312],"category_scores_gemma":[0.005375795,0.0005525011,0.0006250544,0.000363169,0.0008443218,0.002200066,0.001749088,0.002240469,0.00283727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006728724,"about_ca_system_score_gemma":0.0005971094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001730999,"about_ca_topic_score_gemma":0.00351754,"domain_scores_codex":[0.9992638,0.0002175407,0.00002624671,0.0002657047,0.0001520594,0.00007454605],"domain_scores_gemma":[0.9988652,0.0004954287,0.0001005147,0.0002708395,0.0001879801,0.00008004504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007426987,0.0005191616,0.001911432,0.0002728316,0.00008875111,0.0003177749,0.0002199046,0.2743291,0.02892039,0.01367535,0.02583738,0.6531653],"study_design_scores_gemma":[0.00005200323,0.0001384024,0.0001730722,0.0000168846,0.00001037313,0.00006911933,0.00001923061,0.9785765,0.009754991,0.008472629,0.002697567,0.00001924815],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02506124,0.000363441,0.9599842,0.0002050598,0.0001390548,0.0001369774,0.0002730521,0.01134388,0.002492988],"genre_scores_gemma":[0.4716268,0.0002755841,0.5044798,0.0008863783,0.0001136234,0.0004548267,0.003033156,0.00266873,0.01646117],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005719312,"threshold_uncertainty_score":0.01913297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009214591520296031,"score_gpt":0.2673625520725454,"score_spread":0.2581479605522494,"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."}}