{"id":"W2985780925","doi":"10.48550/arxiv.1911.07086","title":"Signed Input Regularization","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Regularization (linguistics); Robustness (evolution); Computer science; Artificial intelligence; Parameterized complexity; Inference; Pattern recognition (psychology); Algorithm; Mathematics; Machine learning","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":[0.001347663,0.001703037,0.001088939,0.0007105586,0.0004959901,0.001496754,0.002291927,0.002429871,0.007022175],"category_scores_gemma":[0.006194076,0.0004546926,0.001035306,0.0008094808,0.001129592,0.002271067,0.002134839,0.002840015,0.002059217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001072919,"about_ca_system_score_gemma":0.001378619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002586293,"about_ca_topic_score_gemma":0.003703927,"domain_scores_codex":[0.9991322,0.0002465992,0.00004312617,0.0002427214,0.0002514658,0.00008390858],"domain_scores_gemma":[0.9985982,0.0004952179,0.0001108536,0.0004212852,0.0003085732,0.00006591711],"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":[0.0002381352,0.0001510286,0.001191677,0.0003090554,0.0001276703,0.0001568976,0.0001013291,0.5124359,0.01638904,0.06904902,0.01735154,0.3824987],"study_design_scores_gemma":[0.000008893971,0.00002521407,0.0001455892,0.00001565193,0.000008358134,0.00004301982,0.000009112712,0.9732055,0.003908846,0.02027015,0.002348627,0.000011007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008377095,0.0002682366,0.9858759,0.0002982469,0.00009632629,0.00004612905,0.000212806,0.001737345,0.00308793],"genre_scores_gemma":[0.5553728,0.0007519809,0.4202149,0.001162239,0.0002132591,0.0003755137,0.002256785,0.001104633,0.01854797],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007022175,"threshold_uncertainty_score":0.0234915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06046118689398607,"score_gpt":0.1787332730307385,"score_spread":0.1182720861367524,"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."}}