{"id":"W2179423374","doi":"10.48550/arxiv.1511.06297","title":"Conditional Computation in Neural Networks for faster models","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":151,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Reinforcement learning; Computer science; Computation; Artificial intelligence; Machine learning; Dropout (neural networks); Regularization (linguistics); Artificial neural network; Deep learning; Algorithm","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":[0.002461442,0.001562764,0.001004581,0.0007685269,0.000547574,0.001507811,0.001797012,0.001657722,0.008246746],"category_scores_gemma":[0.01370003,0.0008550513,0.0009137,0.0009550497,0.001597565,0.003779618,0.002625344,0.005651651,0.001871273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001830674,"about_ca_system_score_gemma":0.001245112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00399167,"about_ca_topic_score_gemma":0.004710406,"domain_scores_codex":[0.9989686,0.0004408933,0.0000463143,0.0001979843,0.0002674113,0.0000787003],"domain_scores_gemma":[0.9955423,0.002935725,0.000264014,0.0009222036,0.0002354488,0.0001001757],"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":[0.0000935812,0.00005570441,0.0004515654,0.0001223992,0.00004377496,0.000059529,0.00007134351,0.700303,0.001614618,0.2501404,0.005229027,0.04181502],"study_design_scores_gemma":[0.000005771729,0.000007161657,0.00002515749,0.000009612006,0.00000353976,0.000007637035,0.000001981886,0.9385125,0.0003721863,0.05985248,0.00119836,0.000003629234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006302695,0.0009637537,0.98685,0.0009784808,0.0001124105,0.0000410771,0.0000873462,0.001200941,0.003463376],"genre_scores_gemma":[0.4521747,0.00192459,0.5297806,0.0009190315,0.0003748052,0.000593136,0.0006320758,0.001403196,0.01219788],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008246746,"threshold_uncertainty_score":0.02758813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1124767320843273,"score_gpt":0.2295061123692834,"score_spread":0.1170293802849561,"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."}}