Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a sequence generation neural network. One of the methods includes obtaining a batch of training examples; for each of the training examples: processing the training network input in the training example using the neural network to generate an output sequence; for each particular output position in the output sequence: identifying a prefix that includes the system outputs at positions before the particular output position in the output sequence, for each possible system output in the vocabulary, determining a highest quality score that can be assigned to any candidate output sequence that includes the prefix followed by the possible system output, and determining an update to the current values of the network parameters that increases a likelihood that the neural network generates a system output at the position that has a high quality score.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a policy neural network used to select actions to be performed by a reinforcement learning agent interacting with an environment. In one aspect, a method includes obtaining path data defining a path through the environment traversed by the agent. A consistency error is determined for the path from a combined reward, first and last soft-max state values, and a path likelihood. A value update for the current values of the policy neural network parameters is determined from at least the consistency error. The value update is used to adjust the current values of the policy neural network parameters.

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

Abstract: A method includes obtaining data identifying a machine learning model to be trained to perform a machine learning task, the machine learning model being configured to receive an input example and to process the input example in accordance with current values of a plurality of model parameters to generate a model output for the input example; obtaining initial training data for training the machine learning model, the initial training data comprising a plurality of training examples and, for each training example, a ground truth output that should be generated by the machine learning model by processing the training example; generating modified training data from the initial training data; and training the machine learning model on the modified training data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network using a priority queue. One of the methods includes maintaining data identifying a set of K output sequences that were previously generated; selecting at least one of the output sequences from the set of output sequences; for each selected output sequence, determining a respective score; determining, for each selected sequence, a respective first update to the current values of the controller parameters; generating a batch of new output sequences using the controller neural network; obtaining a respective reward for each of the new output sequences; determining, from the new output sequences and the output sequences in the maintained data, the K output sequences that have the highest rewards; and modifying the maintained data.

Abstract: The present disclosure provides systems and methods that include or otherwise leverage a machine-learned neural synthesizer model. Unlike a traditional synthesizer which generates audio from hand-designed components like oscillators and wavetables, the neural synthesizer model can use deep neural networks to generate sounds at the level of individual samples. Learning directly from data, the neural synthesizer model can provide intuitive control over timbre and dynamics and enable exploration of new sounds that would be difficult or impossible to produce with a hand-tuned synthesizer. As one example, the neural synthesizer model can be a neural synthesis autoencoder that includes an encoder model that learns embeddings descriptive of musical characteristics and an autoregressive decoder model that is conditioned on the embedding to autoregressively generate musical waveforms that have the musical characteristics one audio sample at a time.

Abstract: A computer-implemented method includes receiving an input data item including a plurality of data elements, and generating a predicted structured output for the input data item. Generating the predicted structured output includes iteratively performing the following operations: receiving a current structured output that assigns, to each of the data elements, a respective current value for each of the one or more categories; processing the input data item and the current output using a value neural network, in which the value neural network has been trained to process the input data item and the current output to generate a value score that is an estimate of how accurately the current output predicts the likelihoods that the elements belong to the one or more categories; and updating the current structured output by adjusting the current values in the current output to increase the value score generated by the value neural network.

Abstract: Non-greedy machine learning for high accuracy is described, for example, where one or more random decision trees are trained for gesture recognition in order to control a computing-based device. In various examples, a random decision tree or directed acyclic graph (DAG) is grown using a greedy process and is then post-processed to recalculate, in a non-greedy process, leaf node parameters and split function parameters of internal nodes of the graph. In various examples the very large number of options to be assessed by the non-greedy process is reduced by using a constrained objective function. In examples the constrained objective function takes into account a binary code denoting decisions at split nodes of the tree or DAG. In examples, resulting trained decision trees are more compact and have improved generalization and accuracy.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for classifying data objects. One of the methods includes obtaining data that associates each term in a vocabulary of terms with a respective high-dimensional representation of the term; obtaining classification data for a data object, wherein the classification data includes a respective score for each of a plurality of categories, and wherein each of the categories is associated with a respective category label; computing an aggregate high-dimensional representation for the data object from high-dimensional representations for the category labels associated with the categories and the respective scores; identifying a first term in the vocabulary of terms having a high-dimensional representation that is closest to the aggregate high-dimensional representation; and selecting the first term as a category label for the data object.

Abstract: An arrangement of elongated nanowires that include titanium silicide or tungsten silicide may be grown on the exterior surfaces of many individual electrically conductive microfibers of much larger diameter. Each of the nanowires is structurally defined by an elongated, centralized titanium silicide or tungsten silicide nanocore that terminates in a distally spaced gold particle and which is co-axially surrounded by a removable amorphous nanoshell. A gold-directed catalytic growth mechanism initiated during a low pressure chemical vapor deposition process is used to grow the nanowires uniformly along the entire length and circumference of the electrically conductive microfibers where growth is intended. The titanium silicide- or tungsten silicide-based nanowires can be used in a variety electrical, electrochemical, and semiconductor applications.

Abstract: An arrangement of elongated nanowires that include titanium silicide or tungsten silicide may be grown on the exterior surfaces of many individual electrically conductive microfibers of much larger diameter. Each of the nanowires is structurally defined by an elongated, centralized titanium silicide or tungsten silicide nanocore that terminates in a distally spaced gold particle and which is co-axially surrounded by a removable amorphous nanoshell. A gold-directed catalytic growth mechanism initiated during a low pressure chemical vapor deposition process is used to grow the nanowires uniformly along the entire length and circumference of the electrically conductive microfibers where growth is intended. The titanium silicide- or tungsten silicide-based nanowires can be used in a variety electrical, electrochemical, and semiconductor applications.