Abstract: A method for reducing idleness in a machine-learning training system can include performing operations by computing devices. A first set of training operations can access and prepare a plurality of training examples of a set of training data. A second set of training operations can train a machine-learned model based at least in part on the set of training data and can include one or more repeat iterations in which at least a portion of the second set of training operations is repeatedly performed such that the training example(s) are repeatedly used to train the machine-learned model. A rate of the repeat iteration(s) can be based at least in part on an echo factor that can be based at least in part on a comparison of a first computational time of the first set of training operations to a second computational time of the second set of training operations.

Abstract: A method for reducing idleness in a machine-learning training system can include performing operations by computing devices. A first set of training operations can access and prepare a plurality of training examples of a set of training data. A second set of training operations can train a machine-learned model based at least in part on the set of training data and can include one or more repeat iterations in which at least a portion of the second set of training operations is repeatedly performed such that the training example(s) are repeatedly used to train the machine-learned model. A rate of the repeat iteration(s) can be based at least in part on an echo factor that can be based at least in part on a comparison of a first computational time of the first set of training operations to a second computational time of the second set of training operations.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing a biological sequence using a neural network. One of the methods includes obtaining data identifying a biological sequence; generating, from the obtained data, an encoding of the biological sequence; processing the encoding using a deep neural network, wherein the deep neural network is configured through training to process the encoding to generate a score distribution over a set of biological labels for the biological sequence; and classifying the biological sequence using the score distribution.

Abstract: A method of determining a final architecture for a neural network (NN) for performing a particular NN task is described.

Abstract: A method is disclosed herein that includes an act of causing a processor to receive a sample, wherein the sample is one of spoken utterance, an online handwriting sample, or a moving image sample. The method also comprises the act of causing the processor to decode the sample based at least in part upon an output of a combination of a deep structure and a context-dependent Hidden Markov Model (HMM), wherein the deep structure is configured to output a posterior probability of a context-dependent unit. The deep structure is a Deep Belief Network consisting of many layers of nonlinear units with connecting weights between layers trained by a pretraining step followed by a fine-tuning step.

Abstract: A method is disclosed herein that includes an act of causing a processor to receive a sample, wherein the sample is one of spoken utterance, an online handwriting sample, or a moving image sample. The method also comprises the act of causing the processor to decode the sample based at least in part upon an output of a combination of a deep structure and a context-dependent Hidden Markov Model (HMM), wherein the deep structure is configured to output a posterior probability of a context-dependent unit. The deep structure is a Deep Belief Network consisting of many layers of nonlinear units with connecting weights between layers trained by a pretraining step followed by a fine-tuning step.