公开(公告)号：US09563840B2

公开(公告)日：2017-02-07

申请号：US14817492

申请日：2015-08-04

Applicant: Google Inc.

Inventor： Alexander Krizhevsky ,  Ilya Sutskever ,  Geoffrey E. Hinton

IPC: G06K9/66 ,  G06T1/20 ,  G06N3/063 ,  G06N3/04 ,  G06K9/62 ,  G06K9/46

CPC classification number: G06N3/04 ,  G06K9/4628 ,  G06K9/6256 ,  G06K9/66 ,  G06N3/0454 ,  G06N3/063 ,  G06N3/08 ,  G06T1/20

Abstract: A parallel convolutional neural network is provided. The CNN is implemented by a plurality of convolutional neural networks each on a respective processing node. Each CNN has a plurality of layers. A subset of the layers are interconnected between processing nodes such that activations are fed forward across nodes. The remaining subset is not so interconnected.

Abstract translation: 提供并行卷积神经网络。 CNN由各自的处理节点上的多个卷积神经网络实现。 每个CNN具有多个层。 层的一个子集在处理节点之间互连，使得激活在节点之间向前馈送。 剩下的子集并不是互连的。

公开(公告)号：US20170032242A1

公开(公告)日：2017-02-02

申请号：US15150091

申请日：2016-05-09

Applicant: Google Inc.

Inventor： Gregory Sean Corrado ,  Ilya Sutskever ,  Jeffrey Adgate Dean

IPC: G06N3/04 ,  G06N3/063

CPC classification number: G06N3/0472 ,  G06F19/00 ,  G06N3/02 ,  G06N3/0427 ,  G06N3/0445 ,  G06N3/063 ,  G16H50/20

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting likelihoods of conditions being satisfied using recurrent neural networks. One of the systems is configured to process a temporal sequence comprising a respective input at each of a plurality of time steps and comprises: one or more recurrent neural network layers; one or more logistic regression nodes, wherein each of the logistic regression nodes corresponds to a respective condition from a predetermined set of conditions, and wherein each of the logistic regression nodes is configured to, for each of the plurality of time steps: receive the network internal state for the time step; and process the network internal state for the time step in accordance with current values of a set of parameters of the logistic regression node to generate a future condition score for the corresponding condition for the time step.

Abstract translation: 方法，系统和装置，包括在计算机存储介质上编码的计算机程序，用于预测利用循环神经网络满足条件的可能性。 系统中的一个被配置为在多个时间步长中的每一个处理包括相应输入的时间序列，并且包括：一个或多个循环神经网络层; 一个或多个逻辑回归节点，其中每个逻辑回归节点对应于来自预定条件集合的相应条件，并且其中每个逻辑回归节点被配置为针对多个时间步骤中的每一个：接收网络 内部状态为时间步; 并根据逻辑回归节点的一组参数的当前值处理时间步长的网络内部状态，以生成时间步长相应条件的未来条件分数。

公开(公告)号：US20170032241A1

公开(公告)日：2017-02-02

申请号：US14810368

申请日：2015-07-27

Applicant: Google Inc.

Inventor： Gregory Sean Corrado ,  Jeffrey Adgate Dean ,  Ilya Sutskever

IPC: G06N3/04 ,  G06N3/10

CPC classification number: G06N3/04 ,  G06N3/0445 ,  G06N3/08 ,  G06N3/10 ,  G16H50/20 ,  G16H50/30

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using recurrent neural networks to analyze health events. One of the methods includes obtaining a first temporal sequence of health events, wherein the first temporal sequence comprises respective health-related data associated with a particular patient at each of a plurality of time steps; processing the first temporal sequence of health events using a recurrent neural network to generate a neural network output for the first temporal sequence; and generating, from the neural network output for the first temporal sequence, health analysis data that characterizes future health events that may occur after a last time step in the temporal sequence.

Abstract translation: 方法，系统和装置，包括在计算机存储介质上编码的计算机程序，用于使用循环神经网络来分析健康事件。 所述方法之一包括获得健康事件的第一时间序列，其中所述第一时间序列在多个时间步骤中的每一个步骤包括与特定患者相关联的各个健康相关数据; 使用循环神经网络处理健康事件的第一时间序列以生成用于第一时间序列的神经网络输出; 以及从用于第一时间序列的神经网络输出生成表征可能在时间序列中的最后时间步长之后发生的未来健康事件的健康分析数据。

公开(公告)号：US09336482B1

公开(公告)日：2016-05-10

申请号：US14810381

申请日：2015-07-27

Applicant: Google Inc.

Inventor： Gregory Sean Corrado ,  Ilya Sutskever ,  Jeffrey Adgate Dean

IPC: G06N3/02 ,  G06N3/04

CPC classification number: G06N3/0472 ,  G06F19/00 ,  G06N3/02 ,  G06N3/0427 ,  G06N3/0445 ,  G06N3/063 ,  G16H50/20

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting likelihoods of conditions being satisfied using recurrent neural networks. One of the systems is configured to process a temporal sequence comprising a respective input at each of a plurality of time steps and comprises: one or more recurrent neural network layers; one or more logistic regression nodes, wherein each of the logistic regression nodes corresponds to a respective condition from a predetermined set of conditions, and wherein each of the logistic regression nodes is configured to, for each of the plurality of time steps: receive the network internal state for the time step; and process the network internal state for the time step in accordance with current values of a set of parameters of the logistic regression node to generate a future condition score for the corresponding condition for the time step.

Abstract translation: 方法，系统和装置，包括在计算机存储介质上编码的计算机程序，用于预测利用循环神经网络满足条件的可能性。 系统中的一个被配置为在多个时间步长中的每一个处理包括相应输入的时间序列，并且包括：一个或多个循环神经网络层; 一个或多个逻辑回归节点，其中每个逻辑回归节点对应于来自预定条件集合的相应条件，并且其中每个逻辑回归节点被配置为针对多个时间步骤中的每一个：接收网络 内部状态为时间步; 并根据逻辑回归节点的一组参数的当前值处理时间步长的网络内部状态，以生成时间步长相应条件的未来条件分数。

公开(公告)号：US20150356401A1

公开(公告)日：2015-12-10

申请号：US14731326

申请日：2015-06-04

Applicant: Google Inc.

Inventor： Oriol Vinyals ,  Quoc V. Le ,  Ilya Sutskever

IPC: G06N3/02

CPC classification number: G06N3/02 ,  G06F17/28 ,  G06N3/0445 ,  G06N3/0454

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating representations of input sequences. One of the methods includes obtaining an input sequence, the input sequence comprising a plurality of inputs arranged according to an input order; processing the input sequence using a first long short term memory (LSTM) neural network to convert the input sequence into an alternative representation for the input sequence; and processing the alternative representation for the input sequence using a second LSTM neural network to generate a target sequence for the input sequence, the target sequence comprising a plurality of outputs arranged according to an output order.

Abstract translation: 方法，系统和装置，包括在计算机存储介质上编码的计算机程序，用于产生输入序列的表示。 所述方法之一包括获得输入序列，所述输入序列包括根据输入顺序排列的多个输入; 使用第一长的短期存储器（LSTM）神经网络来处理输入序列，以将输入序列转换成输入序列的替代表示; 以及使用第二LSTM神经网络处理所述输入序列的替代表示，以生成所述输入序列的目标序列，所述目标序列包括根据输出顺序排列的多个输出。

公开(公告)号：US20150339571A1

公开(公告)日：2015-11-26

申请号：US14817492

申请日：2015-08-04

Applicant: Google Inc.

Inventor： Alexander Krizhevsky ,  Ilya Sutskever ,  Geoffrey E. Hinton

IPC: G06N3/04 ,  G06K9/62 ,  G06T1/20 ,  G06K9/66

CPC classification number: G06N3/04 ,  G06K9/4628 ,  G06K9/6256 ,  G06K9/66 ,  G06N3/0454 ,  G06N3/063 ,  G06N3/08 ,  G06T1/20

Abstract: A parallel convolutional neural network is provided. The CNN is implemented by a plurality of convolutional neural networks each on a respective processing node. Each CNN has a plurality of layers. A subset of the layers are interconnected between processing nodes such that activations are fed forward across nodes. The remaining subset is not so interconnected.

Abstract translation: 提供并行卷积神经网络。 CNN由各自的处理节点上的多个卷积神经网络实现。 每个CNN具有多个层。 层的一个子集在处理节点之间互连，使得激活在节点之间向前馈送。 剩下的子集并不是互连的。