Yam J. Y. F. and Chow T. W. S. Feedforward networks training speed enhancement by optimal initialization of the synaptic coefficients. 概和 here 超级像的一个
What we'd like is an algorithm which lets us find weights and biases so that the output from the network approximates y(x) for all training inputs x. To quantify how well we're achieving this goal we define acost functionC(w,b)≡12n∑x‖y(x)−a‖2.n is the total number of ...
and Kathirvalavakumar, T.: Training feedforward networks using simulta- neous perturbation with dynamic tunneling, Neurocomputing, to appear.Thangavel, P. and Kathirvalavakumar, T.: Training feedforward networks using simultaneous perturbation with dynamic tunneling, Neurocomputing , to appear....
深度学习是机器学习的分支,也就是神经网络,为什么称之为”深度“?因为有很多连接在一起的神经层! 前馈网络 Feedforward Networks 也叫Multilayer Perceptrons(多层感知机),大致的结构如下图所示 其中,每一个节点都可以看做是一个函数,将上一层传过来的输入信息做线性变换,再经过一个激活函数输出给下一层,如下图所...
‣ Flexible — customised architecture for different tasks 缺点：‣ Much slower than classical ML models... but GPU acceleration ‣ Lots of parameters due to vocabulary size ‣ Data hungry, not so good on tiny data sets ‣ Pre-training on big corpora helps Processing math: 100% ...
1.一种最简单的神经网络,各神经元分层排列,每个神经元只与前一层的神经元相连,神经元间的连接带权重,可通过反向传播算法来学习优化。每层接收前一层的输出,并通过一定的权重和偏置进行加权和处理,最终得到本层神经元的输出给到下一层,各层间没有反馈,所以整个网络也没有反馈,信号从输入层向输出层单向传播。
Feed-Forward Artificial Neural NetworksDeep learning is a branch of neural network which has been intensively developed in the last decade. Due to the high-accuracy classification ability, the deep learning algorithms have been widely used in many fields, such as speech recognition, image recognition...
Xavier——Understanding the difficulty of training deep feedforward neural networks 1. 摘要 本文尝试解释为什么在深度的神经网络中随机初始化会让梯度下降表现很差,并且在此基础上来帮助设计更好的算法。 作者发现 sigmoid 函数不适合深度网络,在这种情况下,随机初始化参数会让较深的隐藏层陷入到饱和区域。
理解训练深层前馈神经网络的难度(Undetanding the difficulty of training deep feedforward neural networks ) 译者按:大神bengio 的经典论文之一,不多说 作者:Xavier Glorot Yoshua Bengio 加拿大魁北克 蒙特利尔大学 摘要:在2006年以前,似乎深度多层的神经网络没有被成功训练过。自那以后少数几种算法显示成功地训练了...
A novel training method is proposed that finds the best scaled gradients in each training iteration. The method's implementation uses first order derivatives which makes it scalable and suitable for deep learning and big data. In simulations, the proposed method has similar or less testing error ...