{"id":"W2804866494","doi":"10.1109/mc.2018.2381114","title":"Exploiting Typical Values to Accelerate Deep Learning","year":2018,"lang":"en","type":"article","venue":"Computer","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Toronto","funders":"","keywords":"Exploit; Computer science; Deep learning; Computation; Artificial intelligence; Computer architecture; Machine learning; Computer engineering; Embedded system; Data science; 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":[0.00116213,0.0007073362,0.0005315325,0.0006448845,0.0003891312,0.001522757,0.001319493,0.0005705171,0.005006606],"category_scores_gemma":[0.009205016,0.0005746139,0.0004630496,0.000684251,0.0009407775,0.00478208,0.001548555,0.002360033,0.001116719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001102743,"about_ca_system_score_gemma":0.001158247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001478177,"about_ca_topic_score_gemma":0.003048835,"domain_scores_codex":[0.9993204,0.0001320798,0.00003614958,0.0001107771,0.0003072618,0.0000934568],"domain_scores_gemma":[0.9977198,0.001339383,0.0001703502,0.0004447419,0.0002587886,0.00006688644],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004543433,0.000181539,0.003622511,0.000329186,0.00008065001,0.0001764026,0.0001864642,0.2494252,0.01740315,0.4089726,0.007800127,0.3113678],"study_design_scores_gemma":[0.00001951032,0.00007402079,0.000162469,0.00002767303,0.00001481991,0.00006324382,0.00002459882,0.8173479,0.009064705,0.1691013,0.004086587,0.00001304299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04028575,0.0007154947,0.948351,0.0006492883,0.0001191781,0.00004908857,0.0001001408,0.001579217,0.008150846],"genre_scores_gemma":[0.7100196,0.000837557,0.2832975,0.0002715485,0.0001015073,0.0001204635,0.000209856,0.0003761561,0.004765816],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005006606,"threshold_uncertainty_score":0.01674879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02797206352300208,"score_gpt":0.2836419058607506,"score_spread":0.2556698423377485,"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."}}