Abstract: Disclosed implementations relate to using mutual constraint satisfaction to sample from different stochastic processes and identify coherent inferences across domains. In some implementations, a first domain representation of a semantic concept may be used to conditionally sample a first set of candidate second domain representations of the semantic concept from a first stochastic process. Based on second domain representation(s) of the first set, candidate third domain representations of the semantic concept may be conditionally sampled from a second stochastic process. Based on candidate third domain representation(s), a second set of candidate second domain representations of the semantic concept may be conditionally sampled from a third stochastic process. Pairs of candidate second domain representations sampled across the first and second sets may be evaluated. Based on the evaluation, second domain representation(s) of the semantic concept are selected, e.g., as input for a downstream computer process.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for executing ensemble models that include multiple reservoir computing neural networks. One of the methods includes executing an ensemble model comprising a plurality of reservoir computing neural networks, the ensemble model having been trained by operations comprising, at each training stage in a sequence of training stages: obtaining a current ensemble model that comprises a plurality of current reservoir computing neural networks; determining a respective performance measure for each current reservoir computing neural network in the current ensemble model; determining one or more new reservoir computing neural networks to be added to the current ensemble model based on the performance measures for the current reservoir computing neural networks; and adding the new reservoir computing neural networks to the current ensemble model.

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: 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: 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 computer storage media, for processing representations of EEG measurements. One of the methods includes obtaining a plurality of EEG signal measurements corresponding to respective EEG trials of a user; generating a time-domain representation from the plurality of EEG signal measurements, where the time-domain representation includes a plurality of rows, and where each row corresponds to a different set of one or more EEG signal measurements; applying the time-domain representation as input to a neural network having a plurality of network parameters, final values of the network parameters having been determined by a transfer learning process where the neural network is initially trained to perform an image processing task and the neural network is subsequently trained to perform EEG analysis; and obtaining, from the neural network, a mental health prediction for the user.

Abstract: Systems and processes described herein can expand a limited data set of EEG trials into a larger data set by resampling subsets of EEG trial data. Implementations may employ one or more of a variety of different resampling techniques. For example, a subset of the available training data is selected to form a new set of training data. The subset can be selected using replacement (e.g., a sample can be selected more than once, and thus represented multiple times in the new set of training data). Alternatively the subset can be selected without using replacement (e.g., each sample is able to be selected only once, and thus represented a maximum of one time in the new set of training data).

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining, from one or more electrodes, electroencephalographic (EEG) signals from a user; generating signal vectors from the EEG signals, each signal vector representing one channel of EEG signals. The actions include providing the signal vectors as input data to a variational autoencoder (VAE), wherein the VAE generates a latent representation of the input data, the latent representation having lower dimensionality than the signal vectors, and reconstructs the latent representation into an event related potential (ERP) of the corresponding EEG signal. The actions include providing, for display to a user, a graphical representation of the ERPs.

Abstract: A machine learning system for aggregating electroencephalographic (EEG) data in preparation for downstream analysis via further machine learning models. Machine learning models can be used to assist in diagnosis of various mental health conditions, brain-computer interface, mood detection systems, or other biometric functions. Implementations of the present disclosure, employ a portion of the transformer network (the attention encoder stack) to aggregate EEG trials or EEG data segments, in a data-driven way, by ensuring the important content of each trial is not lost. Each EEG trial to be aggregated is converted into an input embedding, or a vector which numerically represents the data in the trial.

Abstract: A machine learning system for aggregating electroencephalographic (EEG) data, as well as external data, in preparation for downstream analysis via further machine learning models. Machine learning models can be used to assist in diagnosis of various mental health conditions, brain-computer interface, mood detection systems, or other biometric functions. Implementations of the present disclosure, employ a portion of the transformer network (the attention encoder stack) to aggregate EEG trials or EEG data segments, in a data-driven way, by ensuring the important content of each trial is not lost. Each EEG trial to be aggregated is converted into an input embedding, or a vector which numerically represents the data in the trial.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating embeddings of EEG measurements. One of the methods includes obtaining a two-dimensional time-frequency electroencephalogram (EEG) representation corresponding to one or more EEG signal measurements of a user; processing the time-frequency EEG representation using a first neural network having a plurality of first network parameters to generate an embedding of the time-frequency EEG representation, wherein the first neural network has been trained using transfer learning; and providing the embedding of the time-frequency EEG representation to a downstream neural network to generate a mental health prediction for the user.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating embeddings of EEG measurements. One of the methods includes obtaining a plurality of electroencephalogram (EEG) signal measurements of a user, wherein each EEG signal measurement corresponds to one of a plurality of prompt types of an EEG task; generating, from the plurality of EEG signal measurements, a plurality of network inputs each corresponding to a different prompt type of the plurality of prompt types of the EEG task; processing the network inputs using a twin neural network to generate respective network outputs each corresponding to a different prompt type of the plurality of prompt types of the EEG task; and providing the network outputs to a downstream neural network to generate a mental health prediction for the user.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training an auto-encoder to de-noise task specific electroencephalogram (EEG) signals. One of the methods includes training a variational auto-encoder (VAE) including to learn a plurality of parameter values of the VAE by applying, as first training input to the VAE, training data, the training data comprising electroencephalogram (EEG) data representing brain activities of individual persons when performing different tasks; and after the training, adapting the VAE for a specific task by applying, as second training input to the VAE, adaptation data, the adaptation data comprising task-specific EEG data representing brain activities of individual persons when performing the specific task.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining psychological data, generating a psychopathological analysis data structure (PADS), applying a latent factor analysis algorithm to the PADS to obtain a psychopathological latent factor space (PLFS), generating a latent factor graph, and outputting the latent factor graph.