Learning Both Weights And Connections For Efficient Neural Network

Learning Both Weights And Connections For Efficient Neural Network. Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems. Finally, we retrain the network to fine tune the weights of the.

模型剪枝一：Learning both Weights and Connections for Efficient Neural from blog.csdn.net

Next, we prune the unimportant connections. A popular approach for generating sparse neural networks are pruning techniques (lecun et al., 1990; Learning both weights and connections for efficient neural network.

Learning Both Weights And Connections For Efficient Neural Networks.

Finally, we retrain the network to fine tune the weights of the remaining connections. Learning both weights and connections for efficient neural networks. Next, we prune the unimportant.

First, We Train The Network To Learn Which Connections Are Important.

Learning both weights and connections for efficient neural network. Learning both weights and connections for. Learning both weights and connections for efficient neural networks.

Pruning Followed By A Retraining Is One Iteration, After Many Such Iterations The Minimum Number Connections Could Be Found.

Song han 1, jeff pool 2, john tran 2, william j. First, we train the network to learn which connections are important. Also, conventional networks fix the architecture before.

Song Han, Jeff Pool, +1 Author.

Learning the right connections is an iterative process. Next, we prune the unimportant connections. First, we train the network to learn which connections are important.