A method of training a neural network includes encouraging one or more filters in the neural network to have a low rank.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of training a neural network, comprising: encouraging at least one filter in the neural network to have a low rank. 2. The method of claim 1, in which the encouraging comprises applying a regularizer to a filter weight matrix of the at least one filter. 3. The method of claim 2, in which the regularizer is a cost metric that encourages low rank matrices. 4. The method of claim 2, in which the regularizer is a nuclear norm. 5. The method of claim 1, in which encouraging comprises dividing the at least one filter into a linear combination of row filters and column filters. 6. The method of claim 5, further comprising applying a gradient update learned by back propagation to learn the row filters and the column filters. 7. The method of claim 1, further comprising applying a decomposed convolution to the at least one filter to train the neural network. 8. An apparatus for training a neural network, comprising: a memory; and at least one processor coupled to the memory, the at least one processor being configured to encourage at least one filter in the neural network to have a low rank. 9. The apparatus of claim 8, in which the at least one processor is further configured to encourage the at least one filter to have the low rank by applying a regularizer to a filter weight matrix of the at least one filter. 10. The apparatus of claim 9, in which the regularizer is a cost metric that encourages low rank matrices. 11. The apparatus of claim 9, in which the regularizer is a nuclear norm. 12. The apparatus of claim 8, the at least one processor is further configured to encourage the at least one filter to have the low rank by dividing the at least one filter into a linear combination of row filters and column filters. 13. The apparatus of claim 12, in which the at least one processor is further configured to apply a gradient update learned by back propagation to learn the row filters and the column filters. 14. The apparatus of claim 8, in which the at least one processor is further configured to apply a decomposed convolution to the at least one filter to train the neural network. 15. An apparatus for training a neural network, comprising: means for encouraging at least one filter in the neural network to have a low rank; and means for applying a decomposed convolution to the at least one filter to train the neural network. 16. The apparatus of claim 15, in which the means for encouraging applies a regularizer to a filter weight matrix of the at least one filter. 17. The apparatus of claim 16, in which the regularizer is a cost metric that encourages low rank matrices. 18. The apparatus of claim 16, in which the regularizer is a nuclear norm. 19. The apparatus of claim 15, in which the means for encouraging divides the at least one filter into a linear combination of row filters and column filters. 20. The apparatus of claim 19, further comprising means for applying a gradient update learned by back propagation to learn the row filters and the column filters. 21. The apparatus of claim 15, further comprising means for applying a decomposed convolution to the at least one filter to train the neural network. 22. A computer program product for training a neural network, comprising: a non-transitory computer readable medium having encoded thereon program code, the program code comprising: program code to encourage at least one filter in the neural network to have a low rank. 23. The computer program product of claim 22, further comprising program code to encourage the at least one filter to have the low rank by applying a regularizer to a filter weight matrix of the at least one filter. 24. The computer program product of claim 23, in which the regularizer is a cost metric that encourages low rank matrices. 25. The computer program product of claim 23, in which the regularizer is a nuclear norm. 26. The computer program product of claim 22, further comprising program code to encourage the at least one filter to have the low rank by dividing the at least one filter into a linear combination of row filters and column filters. 27. The computer program product of claim 26, further comprising program code to apply a gradient update learned by back propagation to learn the row filters and the column filters. 28. The computer program product of claim 22, further comprising program code to apply a decomposed convolution to the at least one filter to train the neural network.