{"id":"W4403706727","doi":"10.1007/978-3-031-73229-4_20","title":"Salience-Based Adaptive Masking: Revisiting Token Dynamics for Enhanced Pre-training","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Salience (neuroscience); Security token; Masking (illustration); Artificial intelligence; Speech recognition; Computer security","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":[0.0006382472,0.0006732887,0.0007124671,0.0002848847,0.0003030003,0.0007209937,0.001577253,0.0009554592,0.007257489],"category_scores_gemma":[0.002509086,0.0004267502,0.0003722643,0.0003960499,0.000574159,0.001436136,0.001795522,0.001602367,0.001855655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000348023,"about_ca_system_score_gemma":0.0009482584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001974757,"about_ca_topic_score_gemma":0.004062797,"domain_scores_codex":[0.9997526,0.00004976897,0.00001274273,0.00007203178,0.00007041056,0.00004240539],"domain_scores_gemma":[0.9993078,0.0003985579,0.00004145605,0.0001069847,0.00009044434,0.00005488615],"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":[0.000745825,0.0002044178,0.0004550538,0.000260645,0.00006281785,0.0001634622,0.0001743206,0.1908675,0.1927052,0.02940348,0.004669326,0.5802881],"study_design_scores_gemma":[0.00001369471,0.00007776331,0.0001193683,0.00001631547,0.00001551722,0.00005959284,0.00001235084,0.9717051,0.01968217,0.006346236,0.0019409,0.00001096128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009127879,0.0002868605,0.9880375,0.00009594732,0.00009433745,0.00003502948,0.00005590696,0.0006251518,0.001641431],"genre_scores_gemma":[0.449094,0.0006357392,0.537455,0.0003173037,0.0001284952,0.0001289956,0.0003302186,0.0006891472,0.01122112],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007257489,"threshold_uncertainty_score":0.0242787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02327672123787606,"score_gpt":0.2541299440651182,"score_spread":0.2308532228272422,"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."}}