{"id":"W4281676012","doi":"10.1109/isit50566.2022.9834613","title":"MetaSSD: Meta-Learned Self-Supervised Detection","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Symposium on Information Theory (ISIT)","topic":"Wireless Signal Modulation Classification","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Research Foundation of Korea","keywords":"Computer science; Viterbi algorithm; Artificial intelligence; Machine learning; Symbol (formal); Channel (broadcasting); Meta learning (computer science); Detector; Supervised learning; Semi-supervised learning; Artificial neural network; Hidden Markov model; Task (project management)","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":[0.001130638,0.0007971651,0.001188618,0.000757621,0.0003455909,0.000817918,0.002254139,0.0009863315,0.001172857],"category_scores_gemma":[0.003569267,0.0004843075,0.0006419796,0.0005791655,0.000742105,0.001460026,0.0015132,0.001478194,0.0006073122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006104576,"about_ca_system_score_gemma":0.001349202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001577166,"about_ca_topic_score_gemma":0.003151099,"domain_scores_codex":[0.9991192,0.0001973044,0.00005151516,0.0002407761,0.0003008399,0.00009022054],"domain_scores_gemma":[0.9981685,0.0006358279,0.0001989285,0.0004841744,0.0004239382,0.00008854757],"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":[0.000287235,0.0002971989,0.003714236,0.0001698827,0.0002034187,0.0001466884,0.0001706144,0.2718584,0.02048884,0.00829973,0.007885938,0.6864778],"study_design_scores_gemma":[0.00001170112,0.00005385619,0.0001937451,0.000005505194,0.00001036921,0.00005339949,0.000007904749,0.9889947,0.006893175,0.002634145,0.001131225,0.00001039509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0166559,0.0003282085,0.9787184,0.0001628332,0.00008271839,0.00004600684,0.0001068691,0.002865783,0.001033297],"genre_scores_gemma":[0.5544668,0.0002300301,0.4387127,0.0004741106,0.0001160434,0.000150697,0.0006436059,0.0002597079,0.004946192],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002254139,"threshold_uncertainty_score":0.005979478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02604886461964958,"score_gpt":0.2520466213999967,"score_spread":0.2259977567803471,"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."}}