# 创建一个共享参数的卷积神经网络实例 model=SharedCNN()# 打印模型的参数forname,paraminmodel.named_parameters():print(name,param.size())# 输出: # conv1.weight torch.Size([16,1,3,3])# conv1.bias torch.Size([16])# conv2.weight torch.
Cyberbullying (CB) is a challenging issue in social media and it becomes important to effectively identify the occurrence of CB. The recently developed deep learning (DL) models pave the way to design CB classifier models with maximum performance. At the same time, optimal hyperparameter tuning ...
Pytorch implementation of"MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning---arXiv 2019.11.17" Pytorch implementation of"Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks---arXiv 2019.05.23" Pytorch implementation of"A2-Nets: Double Attention ...
In this post we’ll show how to use SigOpt’s Bayesian optimization platform to jointly optimize competing objectives in deep learning pipelines on NVIDIA GPUs more than ten times faster than traditional approaches like random search. A screenshot of the SigOpt web dashboard where users track the...
Deep learning has been applied to various tasks in the field of machine learning and has shown superiority to other common procedures such as kernel methods. To provide a better theoretical understanding of the reasons for its success, we discuss the performance of deep learning and other methods...
(2021). A customized deep learning approach to integrate network-scale online traffic data imputation and prediction. Transportation Research Part C: Emerging Technologies, 132, 103372. 最后总结一句:如果要在小规模图数据上,实现具有可解释性的图卷积,那么这种方式值得一试! 参考文献: Defferrard, M.,...
aA deep learning model is trained to mimic the outputs of a process-based model (PBM). This step is optional since one may also directly implement the model in a DL platform.bWorkflow of the first dPL option, network gA: parameters are inferred by a network (in our case, a separate LS...
In this work we present a design for distributed deep learning training pipelines, focusing on multi-node and multi-GPU environments, where the two different distribution approaches are deployed and benchmarked. We take as proof of concept the 3D U-Net architecture, using the MSD Brain Tumor ...
第一周:深度学习的实践层面 (Practical aspects of Deep Learning) 1.1 训练,验证,测试集(Train / Dev / Test sets) 创建新应用的过程中,不可能从一开始就准确预测出一些信息和其他超级参数,例如:神经网络分多少层;每层含有多少个隐藏单元;学习速率是多少;各层采用哪些激活函数。应用型机器学习是一个高度迭代的...
Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF Surrogates - ili3p/HORD