A method for training a deep neural network (DNN), comprises receiving and formatting speech data for the training, performing Hessian-free sequence training (HFST) on a first subset of a plurality of subsets of the speech data, and iteratively performing the HFST on successive subsets of the plurality of subsets of the speech data, wherein iteratively performing the HFST comprises reusing information from at least one previous iteration.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for training a deep neural network, comprising: a memory and at least one processor coupled to the memory; an input component, executed via the at least one processor, which receives and formats speech data for the training; a training component, executed via the at least one processor, which performs Hessian-free sequence training on a first subset of a plurality of subsets of the speech data received from the input component, and iteratively performs the Hessian-free sequence training on successive subsets of the plurality of subsets of the speech data; wherein the training component reuses information from at least one previous iteration when iteratively performing the Hessian-free sequence training. 2. The system of claim 1, wherein: the information comprises gradient information; and the system further comprises a weighting component operatively coupled to the training component and executed via the at least one processor, which weights the gradient information. 3. The system of claim 2, wherein the weighting component chooses weights based on a difference between a held-out loss of a current iteration and a held-out loss of the at least one previous iteration. 4. The system of claim 2, wherein the training component integrates the weighted gradient information with current gradient information obtained for parameters of a current iteration. 5. The system of claim 2, wherein the weighting component changes weights assigned to the gradient information for different iterations. 6. The system of claim 2, wherein the weighting component dynamically estimates weights assigned to the gradient information to provide a loss function gradient before an iteration takes place. 7. The system of claim 2, wherein the weighting component assigns weights to the gradient information based on a tunable parameter that controls exponentiation of the weights across the plurality of subsets. 8. The system of claim 2, wherein the weighting component uses a weighting function to assign higher weights to gradients with held-out loss values closer to a held-out loss value of a current model, and lower weights to gradients with held-out loss values farther from the held-out loss value of the current model. 9. The system of claim 1, wherein: the training component computes a gradient of a loss for the first subset; the system further comprises a weighting component operatively coupled to the training component and executed via the at least one processor, which: defines a weighting factor of the gradient; and applies the weighting factor to the gradient to result in a weighted gradient; and the training component uses the weighted gradient in connection with sequence training on a successive subset of the plurality of subsets. 10. A computer program product for training a deep neural network, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform a method comprising: receiving and formatting speech data for the training; performing Hessian-free sequence training on a first subset of a plurality of subsets of the speech data; and iteratively performing the Hessian-free sequence training on successive subsets of the plurality of subsets of the speech data; wherein iteratively performing the Hessian-free sequence training comprises reusing information from at least one previous iteration.