Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing an accelerated convolution on sparse inputs. In one aspect, a method comprises receiving sensor data input comprising input features for input spatial locations; and processing the sensor data input using a convolutional neural network having a first convolutional layer with a filter having multiple filter spatial locations to generate a network output comprising output features for output spatial locations, wherein processing the sensor data input comprises: obtaining a rule book tensor that identifies for each filter spatial location (i) a subset of the input features, and (ii) for each input feature in the subset, a respective output feature; for each particular filter spatial location: generating input tile, filter tile, and output tile sets in accordance with the rule book tensor; and generating the output features in the output tile set based on the tile sets.

Abstract: Described herein are systems and methods to identify and address sources of bias in an end-to-end speech model. In one or more embodiments, the end-to-end model may be a recurrent neural network with two 2D-convolutional input layers, followed by multiple bidirectional recurrent layers and one fully connected layer before a softmax layer. In one or more embodiments, the network is trained end-to-end using the CTC loss function to directly predict sequences of characters from log spectrograms of audio. With optimized recurrent layers and training together with alignment information, some unwanted bias induced by using purely forward only recurrences may be removed in a deployed model.

Abstract: Described herein are systems and methods to identify and address sources of bias in an end-to-end speech model. In one or more embodiments, the end-to-end model may be a recurrent neural network with two 2D-convolutional input layers, followed by multiple bidirectional recurrent layers and one fully connected layer before a softmax layer. In one or more embodiments, the network is trained end-to-end using the CTC loss function to directly predict sequences of characters from log spectrograms of audio. With optimized recurrent layers and training together with alignment information, some unwanted bias induced by using purely forward only recurrences may be removed in a deployed model.