Consistent Sparse Deep Learning Theory and Computation 源码

qqendanger11437 8 0 ZIP 2021-04-04 17:04:56

一致的稀疏深度学习:理论与计算我们提出了一种类似于常客的方法来学习稀疏DNN,并证明其在贝叶斯框架下的一致性。稀疏DNN的结构可以在经过训练的贝叶斯神经网络与常规先验混合的基础上,使用基于拉普拉斯近似的边际后验包含概率方法一致地确定。相关刊物孙燕* ,宋其凡*和梁发名, JASA,印刷中。复制论文中的实验结果: 模拟: 产生资料: python Generate_Data.py 回归: python Simulation_Regression.py --data_index 1 --activation 'tanh' python Simulation_Regression.py --data_index 1 --activation 'relu' 回归基准: python Dropout_Regression.py --data_index 1 --activation

文件列表

Consistent-Sparse-Deep-Learning-Theory-and-Computation-main.zip (预估有个22文件)

Consistent-Sparse-Deep-Learning-Theory-and-Computation-main

experiments

DPF_Regression.py 15KB

Spinn_Classification.py 20KB

Spinn_Regression.py 16KB

Simulation_Regression.py 19KB

vb_net.py 29KB

Spinn_structure.py 13KB

run_bnn.py 13KB

Simulation_Structure.py 25KB

Dropout_Regression.py 6KB

Dropout_Classification.py 7KB

cifar_run.py 13KB

cifar_run_vb.py 13KB

resnet_vb.py 9KB

DPF_Classification.py 17KB

resnet.py 8KB

transforms.py 2KB

haha 1B

Generate_Data.py 14KB

Simulation_Classification.py 21KB

process_data.py 2KB

vb_net.py 29KB

README.md 3KB

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Consistent Sparse Deep Learning Theory and Computation 源码

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