Abstract: Disclosed are methods, systems, and other implementations for processing and managing high volume complex data (such as captured neural signals data). The implementations include a method that includes receiving at a first network device, from a remote, second network device, a remote procedure call (RPC) message comprising a first data representation of neural signal data obtained by the second network device and servicing data specifying parameters to cause execution of a first servicing procedure executable on the first network device, performing the first servicing procedure to process the first data representation of the neural signal data to generate result data, and transmitting, by the first remote network device, another RPC message to a destination network device, the other RPC message including the result data.

Abstract: Disclosed are methods, systems, and other implementations for processing, analyzing, and modelling psychotherapy data. The implementations include a method for analyzing psychotherapy data that includes obtaining transcript data representative of spoken dialog in one or more psychotherapy sessions conducted between a patient and a therapist, extracting speech segments from the transcript data related to one or more of the patient or the therapist, applying a trained machine learning topic model process to the extracted speech segments to determine weighted topic labels representative of semantic psychiatric content of the extracted speech segments, and processing the weighted topic labels to derive a psychiatric assessment for the patient.

Abstract: The invention features a computer-implemented biological data classification method executed by one or more processors and including receiving, by the one or more processors, a first biological data set comprising a first plurality of biological sample data collected from a set of patients; processing, by the one or more processors, the first biological data set using a first variational autoencoder (VAE) to generate a first trained VAE comprising a first latent space vector of the first biological data set comprising a plurality of values corresponding to each latent space dimension of the latent space vector, the latent space vector having lower dimensionality than the biological sample data set; receiving, by the one or more processors, a second biological data set comprising a second plurality of biological sample data collected from a patient, different from the set of patients; and generating, by the one or more processors, a latent space representation of the second biological data set based on a first

Abstract: The invention features a computer-implemented biological data prediction method executed by one or more processors including receiving, by the one or more processors, a biomedical data set comprising biomedical data corresponding to a plurality of detected analytes in a biological sample collected from a set of patients at intermittent time intervals, the biomedical data set having a first plurality of feature dimensions; processing, by the one or more processors, the biomedical data set to generate a low-rank tensor having a second plurality of feature dimensions, wherein the second plurality of feature dimensions can be lower than the first plurality of feature dimensions; generating, by the one or more processors, predicted biomedical data along the second plurality of feature dimensions corresponding to the intermittent time intervals; and creating a reconstructed biomedical data set including the predicted biomedical data and the biomedical data along the first plurality of feature dimensions.

Abstract: In one aspect, there is provided a method performed by one or more data processing apparatus for training a neural network, the method including: obtaining a set of training examples, where each training example includes: (i) a training input, and (ii) a target output, and training the neural network on the set of training examples. Training the neural network can include, for each training example: processing the training input using the neural network to generate a corresponding training output, updating current values of at least a set of encoder sub-network parameters and a set of decoder sub-network parameters by a supervised update, and updating current values of at least a set of brain emulation sub-network parameters by an unsupervised update based on correlations between activation values generated by artificial neurons of the neural network during processing of the training input by the neural network.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing defect detection using brain emulation neural networks.

Abstract: In one aspect, there is provided a method performed by one or more data processing apparatus, the method includes: obtaining a network input including a respective data element at each input position in a sequence of input positions, and processing the network input using a neural network to generate a network output that defines a prediction related to the network input, where the neural network includes a sequence of encoder blocks and a decoder block, where each encoder block has a respective set of encoder block parameters, and where the set of encoder block parameters includes multiple brain emulation parameters that, when initialized, represent biological connectivity between multiple biological neuronal elements in a brain of a biological organism.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing remote sensing data using brain emulation neural networks. One of the methods includes obtaining an aerial image of a plurality of agricultural plots; processing the aerial image using an encoder subnetwork of a segmentation neural network to generate an encoder subnetwork output; processing the encoder subnetwork output using a brain emulation subnetwork of the segmentation neural network to generate a brain emulation subnetwork output; processing the brain emulation subnetwork output using a decoder subnetwork of the segmentation neural network to generate a network output that defines a segmentation of the aerial image into a plurality of categories including at least one agricultural plot category; and identifying at least one of the plurality of agricultural plots in the aerial image from the segmentation of the aerial image.

Abstract: In one aspect, there is provided a method performed by one or more data processing apparatus that includes obtaining a network input and processing the network input using a neural network to generate a network output that defines a prediction for the network input. The method further includes processing the network input using an encoding sub-network of the neural network to generate an embedding of the network input, processing the embedding of the network input using a brain hybridization sub-network of the neural network to generate an alternative embedding of the network input, and processing the alternative embedding of the network input using a decoding sub-network of the neural network to generate the network output that defines the prediction for the network input.