{"id":"W2935885634","doi":"10.1145/3331184.3331296","title":"An Axiomatic Approach to Regularizing Neural Ranking Models","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Microsoft (Canada)","funders":"","keywords":"Axiom; Axiomatic system; Ranking (information retrieval); Regularization (linguistics); Machine learning; Computer science; Generalization; Artificial neural network; Artificial intelligence; Relevance (law); Convergence (economics); Training set; Set (abstract data type); Mathematical optimization; Algorithm; Mathematics","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":[0.006202021,0.0008356821,0.0009256052,0.001193421,0.0007513522,0.001803055,0.002624528,0.001738081,0.003576409],"category_scores_gemma":[0.02521108,0.0006452426,0.001408168,0.001431335,0.002321353,0.005010265,0.002331714,0.004893976,0.0009996197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001639942,"about_ca_system_score_gemma":0.00151785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002294407,"about_ca_topic_score_gemma":0.004191916,"domain_scores_codex":[0.9955556,0.002359371,0.0002757986,0.0007027782,0.0009517533,0.0001546497],"domain_scores_gemma":[0.9895579,0.005634049,0.0008190327,0.00252493,0.001274388,0.0001897056],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006172729,0.00008540344,0.0007296126,0.0001386522,0.00007455311,0.0000806782,0.0002214316,0.2034729,0.002050405,0.7342865,0.004916295,0.05388175],"study_design_scores_gemma":[0.00001273848,0.00003612847,0.0001458433,0.00001973498,0.00001406346,0.00005056415,0.00001667842,0.6090588,0.0006781144,0.386653,0.003294539,0.000019815],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004577939,0.0001057128,0.9922126,0.0004680035,0.00003526255,0.00002283286,0.00008625244,0.0002421786,0.002249251],"genre_scores_gemma":[0.3061754,0.0004581948,0.6818885,0.0008676931,0.0003188839,0.0004068784,0.0007388784,0.0003074299,0.008838074],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006202021,"threshold_uncertainty_score":0.03279984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06637320565648477,"score_gpt":0.2703737946978024,"score_spread":0.2040005890413176,"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."}}