A convolutional neural network for classifying time series data uses a dynamic context selection. In one example a method includes receiving a plurality of inputs of different sizes at a convolutional neural network, applying convolution and pooling to each of the inputs to provide a plurality of outputs of different sizes, changing the size of each of the outputs to a selected uniform size, reshaping each of the outputs to a vector, and fully connecting the vectors.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: receiving a plurality of inputs of different sizes at a convolutional neural network; applying convolution and pooling to each of the inputs to provide a plurality of outputs of different sizes; changing the size of each of the outputs to a selected uniform size; reshaping each of the outputs to a vector; and fully connecting the vectors. 2. The method of claim 1, wherein the different sizes of the inputs correspond to different durations. 3. The method of claim 1, further comprising selecting the selected uniform size by applying an optimization function to the plurality of outputs. 4. The method of claim 3, wherein the plurality of outputs are convolutional feature maps and wherein selecting the uniform size comprises selecting a size by determining a size having a maximum energy in a set of the convolutional feature maps. 5. The method of claim 4, wherein determining a maximum energy comprises determining a maximum energy using a sliding window across the feature maps. 6. The method of claim 3, wherein the plurality of outputs are convolutional feature maps and wherein selecting the uniform size comprises selecting a size by determining a size having a maximum entropy in a set of the convolutional feature maps. 7. The method of claim 3, wherein the plurality of outputs are convolutional feature maps and wherein selecting the uniform size comprises selecting a size by determining a size having a minimum entropy in a set of the convolutional feature maps. 8. The method of claim 3, wherein the plurality of outputs are convolutional feature maps and wherein selecting the uniform size comprises; sliding a window across the feature maps; evaluating the windows using an optimization function; and selecting a size based on the optimization function. 9. The method of claim 1, further comprising combining multiple selected contexts. 10. The method of claim 1, wherein the inputs are digital samples of spoken utterances. 11. The method of claim 1, wherein the inputs are digital samples of a video sequence of images. 12. An computing system comprising: a memory to store a plurality of spoken utterances of different durations for a convolutional neural network; a processor to apply convolution and pooling to each of the inputs to provide a plurality of outputs of different sizes, to change the size of each of the outputs to a selected uniform size, to reshape each of the outputs to a vector, and to fully connect the vectors; and a command interface to receive the fully connected vectors and interpret the spoken utterances as commands. 13. The computing system of claim 12, further comprising a microphone to receive the plurality of spoken utterances. 14. The computing system of claim 12, wherein the processor further selects the uniform size by applying an optimization function to the plurality of outputs. 15. The computing system of claim 12, wherein the plurality of outputs are convolutional feature maps and wherein selecting the uniform size comprises selecting a size by determining a size having a maximum energy in a set of the convolutional feature maps. 16. A machine-readable medium having a plurality of instructions that when executed by the machine cause the machine to perform operations comprising: receiving a plurality of inputs of different sizes at a convolutional neural network; applying convolution and pooling to each of the inputs to provide a plurality of outputs of different sizes; changing the size of each of the outputs to a selected uniform size; reshaping each of the outputs to a vector; and fully connecting the vectors. 17. The medium of claim 16, wherein the plurality of outputs are convolutional feature maps the operations further comprising selecting a uniform size by determining a size having a maximum energy in a set of the convolutional feature maps. 18. The medium of claim 16, wherein the plurality of outputs are convolutional feature maps and the operations further comprising selecting a uniform size by determining a size having a maximum entropy in a set of the convolutional feature maps. 19. The medium of claim 16, wherein the plurality of outputs are convolutional feature maps the operations further comprising selecting a uniform size by determining a size having a minimum entropy in a set of the convolutional feature maps. 20. The medium of claim 16, wherein the plurality of outputs are convolutional feature maps the operations further comprising selecting a uniform size by; sliding a window across the feature maps; evaluating the windows using an optimization function; and selecting a size based on the optimization function.