The mirror deep neural networks (DNNs) as described herein recognize patterns in an input signal. Mirror DNNs regularize to a linear function and train very quickly. Mirror DNNs employ a neural network pattern recognizer that receives a set of features extracted from an input signal and inputs the set of features into a multi-layer neural network. The multi-layer neural network has an input layer that receives the set of features, a plurality of intermediate layers, and an output layer that generates a set of output values that are indicative of a recognized pattern exhibited in the input signal. A first and second non-linear equation pair are chosen and applied to intermediate layers of the neural network so as to make the output values that are indicative of a pattern exhibited in the input signal linear.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for recognizing patterns in an input signal, comprising: a neural network pattern recognizer comprising one or more computing devices, said computing devices being in communication with each other via a computer network whenever there is a plurality of computing devices, and a computer program having a plurality of sub-programs executable by said computing devices, wherein the sub-programs configure said computing devices to, receive a set of features extracted from an input signal that is representative of a physical input, input the set of features into a multi-layer neural network comprising an input layer that receives the set of features, a plurality of intermediate layers, and an output layer that generates a set of output values which are indicative of a recognized pattern exhibited in the input signal that are used in an application, wherein one or more of the intermediate layers comprises, input connections from nodes of an immediately preceding layer of the neural network which operate on values received from the immediately preceding layer and then duplicate the values to create a first group of values and a second group of values, wherein the first group of values are operated on using a first non-linear equation to create a modified first group of values and the second group of values are operated on using a second non-linear equation to create modified second group of values, a mirror level of the intermediate layer comprising a first group of nodes which receive the modified first group of values each of which is multiplied by a first half of weight values in a first matrix of weight values, and further comprising a second group of nodes which receive the modified second group of values each of which is multiplied by a second half of weight values in a second matrix of weight values, and the output level of the intermediate layer that comprises nodes each of which receives and sums the values output by the first and second groups of nodes of the mirror level, and wherein the first non-linear equation and the second non-linear equation are chosen so as to make a function computed by the intermediate layer linear with respect to the input of the intermediate layer for some values of the first and second weight matrices. 2. The system of claim 1, wherein the first or the second matrix of at least one intermediate layer is initialized to values that make the function computed by the intermediate layer linear. 3. The system of claim 1, wherein the first or the second matrix is initialized with random values. 4. The system of claim 1, wherein the second matrix of weight values is the mirror of the first matrix of weight values. 5. The system of claim 1, wherein the first matrix of weight values is initialized using weights obtained after training a ReLU neural network using the same training set that is used to train said neural network pattern recognizer, and wherein the second matrix of weight values is initialized with zeros. 6. The system of claim 1, where the intermediate layers are initialized to compute the same function as a residual neural network. 7. The system of claim 1 wherein the input signal comprises a speech signal indicative of a speech input and wherein the features extracted comprise an acoustic feature vector of acoustic features from the speech signal. 8. The system of claim 1 wherein the input signal comprises an image signal indicative of an image input representing a captured image and wherein the features comprise an image feature vector of image features from the image signal. 9. The system of claim 1 wherein the input signal comprises a handwriting recognition signal indicative of a handwriting input and wherein the features comprise a handwriting feature vector of character recognition features from the handwriting recognition signal. 10. The system of claim 1, further comprising iteratively inputting the multiple sets of training features into the multi-layer neural network and adjusting first matrix and second matrix of weight values of each intermediate layer with gradient descent until the output values are sufficient close to the expected output values. 11. The system of claim 10, further comprising a regularizer that is applied during training to affect the first matrix and the second matrix of weights so as to make the output values of the intermediate layer closer to a linear function of its input. 12. A system for recognizing patterns in an input signal, comprising: a neural network pattern recognizer comprising one or more computing devices, said computing devices being in communication with each other via a computer network whenever there is a plurality of computing devices, and a computer program having a plurality of sub-programs executable by said computing devices, wherein the sub-programs configure said computing devices to, receive a set of features extracted from an input signal that is representative of a physical input, input the set of features into a multi-layer neural network comprising an input layer that receives the set of features, a plurality of intermediate layer, and an output layer that generates a set of output values which are indicative of a recognized pattern exhibited in the input signal that are used in an application, wherein one or more of the intermediate layers comprises, input connections from nodes of an immediately preceding layer of the neural network which operate on values received from the immediately preceding layer and then duplicates the values to create a first group of values and a second group of values, and a mirror level of the intermediate layer comprises a first group of nodes which receive the first group of values each of which is multiplied by a first half of weight values in a first matrix of weight values, and further comprises a second group of nodes which receive the second group of values each of which is multiplied by a second half of weight values in a second matrix of weight values, and a non-linear layer wherein the first group of nodes are operated on using a first non-linear equation to create a third group of nodes and the second group of nodes are operated on using a second non-linear equation to create a fourth group of nodes, the output level of the intermediate layer that comprises nodes each of which receives and sums the values output by the third and fourth group of nodes, and wherein the first non-linear equation and the second non-linear equation are chosen so as to make the function computed by the intermediate layer linear with respect to the input for some values of the weight matrices. 13. The system of claim 12, wherein the first or the second matrix of at least one intermediate layer is initialized to values that make the function computed by the intermediate layer linear. 14. The system of claim 12, wherein the first or the second matrix is initialized with random values. 15. The system of claim 12, wherein the second matrix of weight values is the mirror of the first matrix of weight values. 16. The system of claim 12 wherein the input signal comprises a speech signal indicative of a speech input and wherein the features extracted comprise an acoustic feature vector of acoustic features from the speech signal. 17. The system of claim 12, wherein the input signal comprises an image signal indicative of an image input representing a captured image and wherein the features comprise an image feature vector of image features from the image signal. 18. The system of claim 12, further comprising iteratively inputting the multiple sets of training features into the multi-layer neural network and adjusting first matrix and second matrix of weight values of each intermediate layer with gradient descent until the output values are sufficient close to the expected output values. 19. The system of claim 16, further comprising a regularizer that is applied during training to affect the first matrix and the second matrix of weights so as to make the output values of the intermediate layer closer to a linear function of its input. 20. A computer-implemented process for recognizing patterns in an input signal, comprising using a computer to perform the following actions: extracting a set of features from an input signal that is representative of a physical input; inputting the set of features into a multi-layer neural network comprising an input layer that receives the set of features, a plurality of intermediate layers, and an output layer that generates of set of output values which are indicative of a recognized pattern exhibited in the input signal, wherein one or more of the intermediate layers comprises, input connections from nodes of an immediately preceding layer of the neural network which operate on values received from the immediately preceding layer and then duplicate the values to create a first group of values and a second group of values, wherein the first group of values are operated on using a first non-linear equation to create a first group of modified values and the second group of values are operated on using a second non-linear equation to create a second group of modified values, a mirror level of the intermediate layer comprising a first group of nodes which receive the modified first group of values each of which is multiplied by a first half of weight values in a first matrix of weight values to produce a third group of modified values, and further comprising a second group of nodes which receive the modified second group of values each of which is multiplied by a second half of weight values in a second matrix of weight values to produce a fourth group of modified values, and the output level of the intermediate layer that comprises nodes each of which receives and sums the third and fourth group of modified values, and wherein the first non-linear equation and the second non-linear equation are chosen so as to make a function computed by the intermediate layer linear with respect to the input of the intermediate layer for some values of the first and second weight matrices; and outputting values computed at the output level that are indicative of a recognized pattern in the input signal for use with an application.