Xiaoxue Wang, Zixuan Song, Xueting Chen, Yangzi Zhou, Yingze Lou, Tong Liu, Dandan Zhang
Rapid cycle deliberate practice: application in forceps simulation training for gynecology and obstetrics residents
Annals of medicine
multilayer neural networks set current state art many technical classification problems networks still essentially black boxes terms analyzing predicting performance develop statistical theory onelayer perceptron show predict performances surprisingly large variety neural networks different architectures general theory classification perceptrons developed generalizing existing theory analyzing reservoir computing models connectionist models symbolic reasoning known vector symbolic architectures statistical theory offers three formulas leveraging signal statistics increasing detail formulas analytically intractable evaluated numerically description level captures maximum details requires stochastic sampling methods depending network model simpler formulas already yield high prediction accuracy quality theory predictions assessed three experimental settings memorization task echo state networks esns reservoir computing literature collection classification datasets shallow randomly connected networks imagenet dataset deep convolutional neural networks find second description level perceptron theory predict performance types esns could described previously furthermore theory predict deep multilayer neural networks applied output layer methods prediction neural networks performance commonly require train estimator model proposed theory requires first two moments distribution postsynaptic sums output neurons moreover perceptron theory compares favorably methods rely training estimator model