{"id":"W4385424060","doi":"10.3390/make5030045","title":"Efficient Latent Space Compression for Lightning-Fast Fine-Tuning and Inference of Transformer-Based Models","year":2023,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Automatic summarization; Computer science; Inference; Embedding; Encoder; Transformer; Fine-tuning; Artificial intelligence; Pattern recognition (psychology); Voltage","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0004176532,0.0001349997,0.0001860002,0.0001567063,0.0003186765,0.0000693113,0.00008625551,0.00005815455,0.000003142508],"category_scores_gemma":[0.00007427021,0.0001147766,0.00004830583,0.0002642048,0.00003703249,0.00013954,0.00004289062,0.0001759347,0.000002446167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001219749,"about_ca_system_score_gemma":0.00002882103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004049989,"about_ca_topic_score_gemma":0.00002270661,"domain_scores_codex":[0.9991254,0.0001082334,0.0001841009,0.0002926252,0.0001000978,0.0001895647],"domain_scores_gemma":[0.9992335,0.0004056544,0.0001066694,0.00009428371,0.00009457757,0.00006524475],"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":[0.00004806717,0.00007096844,0.00101745,0.0001118172,0.00001532203,0.000001109948,0.001610186,0.7771892,0.04460137,0.001063478,0.00005282701,0.1742182],"study_design_scores_gemma":[0.000484577,0.000162239,0.00119429,0.0001234343,0.00001378056,0.000001743168,0.00003971663,0.9896095,0.006713498,0.0001688333,0.001361851,0.0001265173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06897446,0.0005835943,0.9292352,0.000346718,0.0001829596,0.0001540424,0.000002675528,0.0001104294,0.0004099832],"genre_scores_gemma":[0.9896215,0.00006687779,0.009845049,0.000004620241,0.00005123595,0.00001502597,0.00001040065,0.00001048858,0.0003748475],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.920647,"threshold_uncertainty_score":0.4680453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02609713780614908,"score_gpt":0.2920949468936843,"score_spread":0.2659978090875352,"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."}}