{"id":"W4360595145","doi":"10.1109/tnnls.2022.3227267","title":"Tensor-Empowered Adaptive Learning for Few-Shot Streaming Tasks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Tensor (intrinsic definition); Task (project management); Artificial intelligence; Streaming algorithm; Adaptation (eye); Scratch; Dependency (UML); Machine learning; Mathematics; Upper and lower bounds","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","sts"],"consensus_categories":[],"category_scores_codex":[0.0008111374,0.0003290233,0.0003839026,0.0003217014,0.001539338,0.0005098465,0.0003208475,0.0001820346,0.000006866281],"category_scores_gemma":[0.00004343766,0.0003185065,0.0001750808,0.0007924425,0.00006600383,0.0004132566,0.000008379698,0.001171766,0.00002587617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004918364,"about_ca_system_score_gemma":0.00003009127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004821179,"about_ca_topic_score_gemma":0.000008206325,"domain_scores_codex":[0.9973966,0.0004474387,0.0004405156,0.0007087861,0.0003344091,0.0006721856],"domain_scores_gemma":[0.9981443,0.001020385,0.0002323256,0.0002650075,0.0001230293,0.0002149774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005117074,0.00001930839,0.00007532515,0.00002583158,0.00004530575,0.00001187771,0.0009207138,0.9544689,0.0001961592,0.0004491224,0.0001464795,0.04358979],"study_design_scores_gemma":[0.000684421,0.000468941,0.000268624,0.0001053845,0.000022392,0.00002980259,0.00139929,0.9927879,0.00001986579,0.00001351032,0.003844466,0.0003554068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01735173,0.0001719832,0.9790421,0.0002149788,0.001332671,0.0005010897,0.000002933817,0.001050298,0.0003322873],"genre_scores_gemma":[0.991884,0.00008795126,0.001088182,0.0001006196,0.0001683497,0.0001449006,0.00001013722,0.00005515263,0.006460742],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9779539,"threshold_uncertainty_score":0.9999267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03811174194860487,"score_gpt":0.2646603826212851,"score_spread":0.2265486406726802,"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."}}