{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004042539,0.0009220203,0.0006349963,0.0006149802,0.0002536079,0.0005480233,0.0009401852,0.0005318089,0.001701873],"category_scores_gemma":[0.002330442,0.0002608128,0.0002889437,0.0008150847,0.0004024724,0.001436367,0.0009447878,0.001213613,0.0004278761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005788935,"about_ca_system_score_gemma":0.0004782671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003436877,"about_ca_topic_score_gemma":0.004996845,"domain_scores_codex":[0.9996423,0.00004459175,0.00002319075,0.00006803496,0.000182523,0.00003927406],"domain_scores_gemma":[0.9995028,0.0001939226,0.00003871973,0.0001313438,0.0001156221,0.0000175969],"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":[0.000294234,0.00009741481,0.0007858979,0.0001482585,0.000052942,0.0002646174,0.0001098357,0.466878,0.04357086,0.01219294,0.008858557,0.4667465],"study_design_scores_gemma":[0.000009038647,0.00002092769,0.0001733131,0.000009408025,0.000005786665,0.00003744801,0.00001083699,0.983053,0.010963,0.004504604,0.001206404,0.000006265649],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07506056,0.002081832,0.9122382,0.00055816,0.0002752037,0.0000886118,0.0005302783,0.003834889,0.005332356],"genre_scores_gemma":[0.7483624,0.001194331,0.2415209,0.0003423187,0.0001806816,0.0001731004,0.001247046,0.000271085,0.006708049],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003436877,"threshold_uncertainty_score":0.006833732,"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."}}