{"id":"W2949722522","doi":"10.48550/arxiv.1608.07365","title":"Scalable Compression of Deep Neural Networks","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Computer science; Scalability; Artificial neural network; Upgrade; Distributed computing; Deep neural networks; Artificial intelligence; Operating system","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009177955,0.0002707191,0.0003401776,0.0001342039,0.000141022,0.00003444863,0.002295187,0.0002355625,0.00002233951],"category_scores_gemma":[0.00001125142,0.0002579341,0.0001744017,0.0005554722,0.0001628308,0.0003635637,0.003004435,0.0004338686,0.0000235777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009125178,"about_ca_system_score_gemma":0.00003077441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001223313,"about_ca_topic_score_gemma":0.000007751657,"domain_scores_codex":[0.998238,0.0001097533,0.0002441984,0.0009547887,0.00008349084,0.0003698301],"domain_scores_gemma":[0.9974059,0.0002039416,0.0004126527,0.001653754,0.0001611034,0.0001626635],"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":[0.00001510632,0.00004487646,0.0009126991,0.00001957525,0.00001976859,0.00002556981,0.00001663637,0.8873948,0.000108012,0.1060709,0.0002037323,0.005168271],"study_design_scores_gemma":[0.000253493,0.00002176245,0.0004712265,0.00006235154,0.00001982669,0.000002669897,0.000003232234,0.9371728,0.0002252386,0.06130721,0.0002006862,0.0002595361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01928757,0.0001469866,0.9777777,0.0001387273,0.0004389211,0.0003002271,0.000006609137,0.0002338894,0.001669427],"genre_scores_gemma":[0.9955399,0.0001231047,0.003711611,0.00006726201,0.00009980224,0.000002043761,0.000008423692,0.00001790149,0.0004299215],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9762524,"threshold_uncertainty_score":0.9999873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04759501308342212,"score_gpt":0.1993986158572703,"score_spread":0.1518036027738482,"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."}}