公开(公告)号：US20230222176A1

公开(公告)日：2023-07-13

申请号：US17574915

申请日：2022-01-13

Applicant: X Development LLC

Inventor： Garrett Raymond Honke ,  Baihan Lin ,  Anupama Thubagere Jagadeesh

IPC: G06K9/62 ,  G01N33/68 ,  G01N33/50

CPC classification number: G06K9/6256 ,  G06K9/623 ,  G01N33/6848 ,  G01N33/5038

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 latent space vector.

公开(公告)号：US20230206059A1

公开(公告)日：2023-06-29

申请号：US17564536

申请日：2021-12-29

Applicant: X Development LLC

Inventor： Sarah Ann Laszlo ,  Lam Thanh Nguyen ,  Baihan Lin

IPC: G06N3/08 ,  G06N3/063

CPC classification number: G06N3/08 ,  G06N3/063

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.

公开(公告)号：US20230196059A1

公开(公告)日：2023-06-22

申请号：US17557618

申请日：2021-12-21

Applicant: X Development LLC

Inventor： Sarah Ann Laszlo ,  Lam Thanh Nguyen ,  Baihan Lin ,  Julia Renee Watson ,  Garrett Raymond Honke

IPC: G06N3/00

CPC classification number: G06N3/008

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.

公开(公告)号：US20230186059A1

公开(公告)日：2023-06-15

申请号：US17547107

申请日：2021-12-09

Applicant: X Development LLC

Inventor： Sarah Ann Laszlo ,  Estefany Kelly Buchanan ,  Baihan Lin

IPC: G06N3/06 ,  G06N3/04

CPC classification number: G06N3/061 ,  G06N3/0472

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.

公开(公告)号：US20230223144A1

公开(公告)日：2023-07-13

申请号：US17574834

申请日：2022-01-13

Applicant: X Development LLC

Inventor： Garrett Raymond Honke ,  Baihan Lin ,  Anupama Thubagere Jagadeesh

IPC: G16H50/20 ,  G16B50/00 ,  G16H50/50 ,  G16H10/40

CPC classification number: G16H50/20 ,  G16B50/00 ,  G16H10/40 ,  G16H50/50

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.

公开(公告)号：US20230196541A1

公开(公告)日：2023-06-22

申请号：US17559641

申请日：2021-12-22

Applicant: X Development LLC

Inventor： Sarah Ann Laszlo ,  Lam Thanh Nguyen ,  Baihan Lin

IPC: G06T7/00 ,  G06N3/04 ,  G06N3/00

CPC classification number: G06T7/0004 ,  G06N3/0454 ,  G06N3/008 ,  G06T2207/20084 ,  G06T2207/30108 ,  G06T2207/20081

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing defect detection using brain emulation neural networks. One of the methods includes obtaining an image of a manufactured article; processing the image of the manufactured article using an encoder subnetwork of a defect detection neural network to generate an encoder subnetwork output; processing the encoder subnetwork output using a brain emulation subnetwork of the defect detection neural network to generate a brain emulation subnetwork output, wherein the brain emulation subnetwork has an architecture that comprises brain emulation parameters that, when initialized, represent biological connectivity between biological neuronal elements in a brain of a biological organism; processing the brain emulation subnetwork output using a decoder subnetwork of the defect detection neural network to generate a network output that predicts whether the manufactured article includes a defect; and taking an action based on the network output.

公开(公告)号：US20230186622A1

公开(公告)日：2023-06-15

申请号：US17550506

申请日：2021-12-14

Applicant: X Development LLC

Inventor： Sarah Ann Laszlo ,  Lam Thanh Nguyen ,  Baihan Lin

IPC: G06V20/10 ,  G06N3/04 ,  G06V20/17 ,  G06V10/82 ,  G06V10/44 ,  G06T7/11 ,  G06T7/73 ,  G06V10/764

CPC classification number: G06V20/188 ,  G06N3/0454 ,  G06V20/17 ,  G06V10/82 ,  G06V10/44 ,  G06T7/11 ,  G06T7/73 ,  G06V10/764 ,  G06T2207/10032 ,  G06T2207/20084 ,  G06T2207/30188 ,  G06T2207/30244 ,  G06T2207/20221 ,  G06T2207/20081

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.