Methods and systems of training a neural network includes training a neural network based on training data. Weights of a layer of the neural network are multiplied by an attrition factor. A block of weights is pruned from the layer if the block of weights in the layer has a contribution to an output of the layer that is below a threshold.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of training a neural network, comprising: training a neural network based on training data; multiplying weights of a layer of the neural network by an attrition factor; and pruning a block of weights from the layer if the block of weights in the layer has a contribution to an output of the layer that is below a threshold. 2. The method of claim 1, wherein the attrition factor is a number less than one. 3. The method of claim 1, wherein the contribution of a block of weights to the output of the layer is calculated as a percentage of a sum of absolute weights of the weights in the layer made up by a sum of absolute weights of the weights in the block of weights. 4. The method of claim 1, further comprising pruning a filter in a subsequent layer in the neural network that corresponds to the pruned block of weights. 5. The method of claim 4, further comprising pruning a block of weights in a subsequent layer in the neural network that corresponds to the pruned filter. 6. The method of claim 1, wherein the neural network is a convolutional neural network. 7. The method of claim 1, wherein training, multiplying, and pruning are repeated until output of the neural network is within a threshold difference from an expected output for a validation data set. 8. The method of claim 1, wherein training the neural network comprises a forward pass using the training data, a backward pass, and a learning pass that updates weights of the neural network. 9. The method of claim 1, wherein pruning a block of weights comprises removing a column or row of an array of weights. 10. A method of training a neural network, comprising: training a convolutional neural network based on training data; multiplying weights of a layer of the neural network by a number less than one; and pruning a block of weights from the layer, pruning a filter corresponding to the block of weights in a subsequent layer in the neural network, and pruning a block of weights that corresponds to the pruned filter in a subsequent layer in the neural network, if the block of weights in the layer has a contribution to an output of the layer that is below a threshold, wherein the contribution of a block of weights to the output of the layer is calculated as a percentage of a sum of absolute weights of the weights in the layer made up by a sum of absolute weights of the weights in the block of weights. 11. A system for training a neural network, comprising: a neural network; a training module configured to train the neural network based on training data; and a pruning module configured to multiply weights of a layer of the neural network by an attrition factor and to prune a block of weights from the layer if the block of weights in the layer has a contribution to an output of the layer that is below a threshold. 12. The system of claim 11, wherein the attrition factor is a number less than one. 13. The system of claim 11, wherein the pruning module is further configured to calculate the contribution of a block of weights to the output of the layer as a percentage of a sum of absolute weights of the weights in the layer made up by a sum of absolute weights of the weights in the block of weights. 14. The system of claim 11, further wherein the pruning module is further configured to prune a filter in a subsequent layer in the neural network that corresponds to the pruned block of weights. 15. The system of claim 14, wherein the pruning module is further configured to prune a block of weights in a subsequent layer in the neural network that corresponds to the pruned filter. 16. The system of claim 11, wherein the neural network is a convolutional neural network. 17. The system of claim 11, wherein the training module and the pruning module are further configured to repeat training, multiplying, and pruning until output of the neural network is within a threshold difference from an expected output for a validation data set. 18. The system of claim 11, wherein the training module is further configured to train the neural network using a forward pass using the training data, a backward pass, and a learning pass that updates weights of the neural network. 19. The system of claim 11, wherein the pruning module is further configured to remove a column or row of an array of weights.