参考资料 [1]http://stats.stackexchange.com/questions/47590/what-are-good-initial-weights-in-a-neural-network [2] Bengio, Yoshua. “Practical recommendations for gradient-based training of deep architectures.” Neural Networks: Tricks of the Trade. Springer Berlin Heidelberg, 2012. 437-478. [3]...
2.1 Learning Neural Networks Let us examine how neural network weights are actually learned. For the logistic sigmoid function, say, fx=11+e−x which if being plotted in a graph would be as shown in Fig. 2. Sign in to download full-size image Fig. 2. Graph of the standard logistic ...
Hyperspherical Weight Uncertainty in Neural Networks Bayesian neural networks learn a posterior probability distribution over the weights of the network to estimate the uncertainty in predictions. Parameteriz... B Ghoshal,A Tucker 被引量: 0发表: 2021年 Implicit Weight Uncertainty in Neural Networks Mode...
也就是说,Xavier init的策略,可以使得我们维持 inputs 的分布!这是我们乐于看到的,这样的话,我们可以进行很长的乘法序列计算,也不会改变数据分布,这允许我们去训练真正的deep neural network。 注意,Xavier init 对于我们的case 来说是足够有效了,因为我们没有使用任何的激活函数,如果我们使用了像 ReLu 这样的激活...
【阿里&北大】Meta-Weight Graph Neural Network: Push the Limits Beyond Global Homophily 一句话介绍 传统图网络中的聚合函数在不同node之间是share parameters的,这种方式可以有效建模图节点的同质性(homophily relational data),但考虑到图节点不全是同质的,甚至同一类节点的数据分布也有差异,使用相同的graph ...
Proper initialization of neural network weights is critical problem. Many methods have been proposed for initialization of neural network weights. In this paper a direct neural control strategy is used to control the process. The study of effect of initialization of weights in neural network control...
In this paper, adaptive filtering approaches of colored noise based on the Kalman filter structure using neural networks are proposed, which need not exten... SS Xiong,ZY Zhou - 《IEEE Transactions on Instrumentation & Measurement》 被引量: 53发表: 2003年 Neural Network with Type-2 Fuzzy Weig...
Convolutional neural network (CNN)Lightweight CNNImage recognitionLow-redundancyModel sizeComputation complexityDeep neural networks have achieved great success in many tasks of pattern recognition. However, large model size and high cost in computation limit their applications in resource-limited systems. ...
Xavier Glorot et al., Understanding the Difficult of Training Deep Feedforward Neural Networks Kaiming He et al., Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classfication Sergey Ioffe et al., Batch Normalization: Accelerating Deep Network Training by Reducing Internal...
2.2 Inception:Remaining building blocks in feature generation Inception没有广泛的使用在现存的网络中,也没有验证其有效性。我们发现Inception是用于捕获输入图像中小目标和大目标的最具有cost-effective(成本效益)的building blocks之一。为了学习捕获大目标的视觉模式,CNNs的输出特征应该对应于足够大的感受野,这可以很容...