Learn- ing halfspaces and neural networks with random initialization. arXiv preprint arXiv:1511.07948, 2015.Yuchen Zhang, Jason D Lee, Martin J Wainwright, and Michael I Jordan. Learning halfspaces and neural networks with random initialization. arXiv preprint arXiv:1511.07948, 2015....
In order to break this expressiveness barrier, GNNs have been enhanced with random node initialization (RNI), where the idea is to train and run the models with randomized initial node features. In this work, we analyze the expressive power of GNNs with RNI, and prove that these models are...
(2021). Uncertainty Estimation in Hydrogeological Forecasting with Neural Networks: Impact of Spatial Distribution of Rainfalls and Random Initialization of the Model. Water, 13(12), 1690. https://doi.org/10.3390/w13121690 Note that from the first issue of 2016, this journal uses article ...
From the random initialization of weights in an artificial neural network, to the splitting of data into random train and test sets, to the random shuffling of a training dataset in stochastic gradient descent, generating random numbers and harnessing randomness is a required skill. In this tutoria...
Convolutional neural networks are sensitive to the random initialization of filters. We call this The Filter Lottery (TFL) because the random numbers used to initialize the network determine if you will "win" and converge to a satisfactory local minimum. This issue forces networks to contain more...
4、compare_initializations.py: 比较了四种初始化方法(初始化为0,随机初始化,Xavier initialization和He initialization),具体效果见CSDN博客:https://blog.csdn.net/u012328159/article/details/80025785 5、deep_neural_network_with_L2.py: 带L2正则项正则项的网络(在deep_neural_network.py的基础上增加了L2正则项...
Several simulation results show that the proposed initialization method performs much better than the conventional random initialization method in the batch mode... MC Kim,CH Choi - 《Neural Processing Letters》 被引量: 27发表: 1997年 Generation of reproducible random initial states in RTL simulators...
applications in the field of cryptography—for individual user applications;—for generation of random initialization sequences (so-called seeds) for encryption algorithms, authentication or digital signature;—for key generation (for asymmetric and symmetric cryptography, e.g., for the One Time-Pad ci...
thanks to the iterative message passing in ESGNN, node internal states gradually integrate graph information such as topology along iterations, yielding the unique node embeddings shown in the last time step of Fig.3b(see Extended Data Fig.2and Supplementary Note5for the initialization and storage ...
今天(2018-9-23)重构了神经网络架构(见 deep_neural_network_release.py),把各功能函数分离出来,耦合度更低,结构更清楚,bp过程更加清晰。推荐此版本,用1-10时,可用此版本替换相应代码 1、deep_neural_network_v1.py:自己实现的最简单的深度神经网络(多层感知机),不包含正则化,dropout,动量等...总之是最基本...