Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing neural architecture search for machine learning models. In one aspect, a method comprises receiving training data for a machine learning, generating a plurality of candidate neural networks for performing the machine learning task, wherein each candidate neural network comprises a plurality of instances of a layer block composed of a plurality of layers, for each candidate neural network, selecting a respective type for each of the plurality of layers from a set of layer types that comprises, training the candidate neural network and evaluating performance scores for the trained candidate neural networks as applied to the machine learning task, and determining a final neural network for performing the machine learning task based at least on the performance scores for the candidate neural networks.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using recurrent neural networks. One of the systems includes a main recurrent neural network comprising one or more recurrent neural network layers and a respective hyper recurrent neural network corresponding to each of the one or more recurrent neural network layers, wherein each hyper recurrent neural network is configured to, at each of a plurality of time steps: process the layer input at the time step to the corresponding recurrent neural network layer, the current layer hidden state of the corresponding recurrent neural network layer, and a current hypernetwork hidden state of the hyper recurrent neural network to generate an updated hypernetwork hidden state.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a document classification neural network. One of the methods includes training an autoencoder neural network to autoencode input documents, wherein the autoencoder neural network comprises the one or more LSTM neural network layers and an autoencoder output layer, and wherein training the autoencoder neural network comprises determining pre-trained values of the parameters of the one or more LSTM neural network layers from initial values of the parameters of the one or more LSTM neural network layers; and training the document classification neural network on a plurality of training documents to determine trained values of the parameters of the one or more LSTM neural network layers from the pre-trained values of the parameters of the one or more LSTM neural network layers.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting future patient health using a recurrent neural network. In particular, at each time step, a network input for the time step is processed using a recurrent neural network to update a hidden state of the recurrent neural network. Specifically, the hidden state of the recurrent neural network is partitioned into a plurality of partitions and the plurality of partitions comprises a respective partition for each of a plurality of possible observational features.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for searching for an architecture for a neural network that performs a multi-modal task that requires operating on inputs that each include data from multiple different modalities.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting future patient health using neural networks. One of the methods includes receiving electronic health record data for a patient; generating a respective observation embedding for each of the observations, comprising, for each clinical note: processing the sequence of tokens in the clinical note using a clinical note embedding LSTM to generate a respective token embedding for each of the tokens; and generating the observation embedding for the clinical note from the token embeddings; generating an embedded representation, comprising, for each time window: combining the observation embeddings of observations occurring during the time window to generate a patient record embedding; and processing the embedded representation of the electronic health record data using a prediction recurrent neural network to generate a neural network output that characterizes a future health status of the patient.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for structuring and training a recurrent neural network. This describes a technique that improves the ability to capture long term dependencies in recurrent neural networks by adding an unsupervised auxiliary loss at one or more anchor points to the original objective. This auxiliary loss forces the network to either reconstruct previous events or predict next events in a sequence, making truncated backpropagation feasible for long sequences and also improving full backpropagation through time.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a document classification neural network. One of the methods includes training an autoencoder neural network to autoencode input documents, wherein the autoencoder neural network comprises the one or more LSTM neural network layers and an autoencoder output layer, and wherein training the autoencoder neural network comprises determining pre-trained values of the parameters of the one or more LSTM neural network layers from initial values of the parameters of the one or more LSTM neural network layers; and training the document classification neural network on a plurality of training documents to determine trained values of the parameters of the one or more LSTM neural network layers from the pre-trained values of the parameters of the one or more LSTM neural network layers.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using recurrent neural networks. One of the systems includes a main recurrent neural network comprising one or more recurrent neural network layers and a respective hyper recurrent neural network corresponding to each of the one or more recurrent neural network layers, wherein each hyper recurrent neural network is configured to, at each of a plurality of time steps: process the layer input at the time step to the corresponding recurrent neural network layer, the current layer hidden state of the corresponding recurrent neural network layer, and a current hypernetwork hidden state of the hyper recurrent neural network to generate an updated hypernetwork hidden state.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting future patient health using neural networks. One of the methods includes receiving electronic health record data for a patient; generating a respective observation embedding for each of the observations, comprising, for each clinical note: processing the sequence of tokens in the clinical note using a clinical note embedding LSTM to generate a respective token embedding for each of the tokens; and generating the observation embedding for the clinical note from the token embeddings; generating an embedded representation, comprising, for each time window: combining the observation embeddings of observations occurring during the time window to generate a patient record embedding; and processing the embedded representation of the electronic health record data using a prediction recurrent neural network to generate a neural network output that characterizes a future health status of the patient.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network. One of the methods includes training the neural network for one or more training steps in accordance with a current learning rate; generating a training dynamics observation characterizing the training of the trainee neural network on the one or more training steps; providing the training dynamics observation as input to a controller neural network that is configured to process the training dynamics observation to generate a controller output that defines an updated learning rate; obtaining as output from the controller neural network the controller output that defines the updated learning rate; and setting the learning rate to the updated learning rate.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating document vector representations. One of the methods includes obtaining a new document; selecting a plurality of new document word sets; and determining a vector representation for the new document using a trained neural network system, wherein the trained neural network system comprises: a document embedding layer and a classifier, and wherein determining the vector representation for the new document using the trained neural network system comprises iteratively providing each of the plurality of new document word sets to the trained neural network system to determine the vector representation for the new document using gradient descent.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating document vector representations. One of the methods includes obtaining a new document; selecting a plurality of new document word sets; and determining a vector representation for the new document using a trained neural network system, wherein the trained neural network system comprises: a document embedding layer and a classifier, and wherein determining the vector representation for the new document using the trained neural network system comprises iteratively providing each of the plurality of new document word sets to the trained neural network system to determine the vector representation for the new document using gradient descent.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting future patient health using neural networks. One of the methods includes receiving electronic health record data for a patient; generating a respective observation embedding for each of the observations, comprising, for each clinical note: processing the sequence of tokens in the clinical note using a clinical note embedding LSTM to generate a respective token embedding for each of the tokens; and generating the observation embedding for the clinical note from the token embeddings; generating an embedded representation, comprising, for each time window: combining the observation embeddings of observations occurring during the time window to generate a patient record embedding; and processing the embedded representation of the electronic health record data using a prediction recurrent neural network to generate a neural network output that characterizes a future health status of the patient.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a document classification neural network. One of the methods includes training an autoencoder neural network to autoencode input documents, wherein the autoencoder neural network comprises the one or more LSTM neural network layers and an autoencoder output layer, and wherein training the autoencoder neural network comprises determining pre-trained values of the parameters of the one or more LSTM neural network layers from initial values of the parameters of the one or more LSTM neural network layers; and training the document classification neural network on a plurality of training documents to determine trained values of the parameters of the one or more LSTM neural network layers from the pre-trained values of the parameters of the one or more LSTM neural network layers.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for structuring and training a recurrent neural network. This describes a technique that improves the ability to capture long term dependencies in recurrent neural networks by adding an unsupervised auxiliary loss at one or more anchor points to the original objective. This auxiliary loss forces the network to either reconstruct previous events or predict next events in a sequence, making truncated backpropagation feasible for long sequences and also improving full backpropagation through time.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for decompounding compound words are disclosed. In one aspect, a method includes obtaining a token that includes a sequence of characters, identifying two or more candidate sub-words that are constituents of the token, and one or more morphological operations that are required to transform the sub-words into the token, where at least one of the morphological operations involves a use of a non-dictionary word, and determining a cost associated with each sub-word and a cost associated with each morphological operation.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating tasks from user observations. One of the methods includes segmenting a plurality of observations associated with a user of a user device into a plurality of tasks previously engaged in by the user; and generating a respective task presentation for each of the plurality of tasks for presentation to the user.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating tasks from user observations. One of the methods includes segmenting a plurality of observations associated with a user of a user device into a plurality of tasks previously engaged in by the user; and generating a respective task presentation for each of the plurality of tasks for presentation to the user.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for decompounding compound words are disclosed. In one aspect, a method includes obtaining a token that includes a sequence of characters, identifying two or more candidate sub-words that are constituents of the token, and one or more morphological operations that are required to transform the sub-words into the token, where at least one of the morphological operations involves a use of a non-dictionary word, and determining a cost associated with each sub-word and a cost associated with each morphological operation.