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    WHITENED NEURAL NETWORK LAYERS
    3.
    发明申请
    WHITENED NEURAL NETWORK LAYERS 审中-公开
    白色神经网络层

    公开(公告)号：US20160358073A1

    公开(公告)日：2016-12-08

    申请号：US15174020

    申请日：2016-06-06

    Applicant: Google Inc.

    Inventor： Guillaume Desjardins ,  Karen Simonyan ,  Koray Kavukcuoglu ,  Razvan Pascanu

    IPC: G06N3/08 ,  G06N99/00 ,  G06N3/04

    CPC classification number: G06N3/08 ,  G06N3/04 ,  G06N3/084 ,  G06N20/00

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using a neural network system that includes a whitened neural network layer. One of the methods includes receiving an input activation generated by a layer before the whitened neural network layer in the sequence; processing the received activation in accordance with a set of whitening parameters to generate a whitened activation; processing the whitened activation in accordance with a set of layer parameters to generate an output activation; and providing the output activation as input to a neural network layer after the whitened neural network layer in the 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，用于使用包含白化神经网络层的神经网络系统处理输入。 其中一种方法包括在序列中接收由白化神经网络层之前的层产生的输入激活; 根据一组增白参数来处理接收到的激活以产生白化激活; 根据一组层参数处理白化激活以产生输出激活; 并且在序列中的白化神经网络层之后，将输出激活提供给神经网络层的输入。

    DISTRIBUTED TRAINING OF REINFORCEMENT LEARNING SYSTEMS
    4.
    发明申请
    DISTRIBUTED TRAINING OF REINFORCEMENT LEARNING SYSTEMS 审中-公开
    加强学习系统的分布式培训

    公开(公告)号：US20160232445A1

    公开(公告)日：2016-08-11

    申请号：US15016173

    申请日：2016-02-04

    Applicant: Google Inc.

    Inventor： Praveen Deepak Srinivasan ,  Rory Fearon ,  Cagdas Alcicek ,  Arun Sarath Nair ,  Samuel Blackwell ,  Vedavyas Panneershelvam ,  Alessandro De Maria ,  Volodymyr Mnih ,  Koray Kavukcuoglu ,  David Silver ,  Mustafa Suleyman

    IPC: G06N3/08 ,  G06N3/04

    CPC classification number: G06N3/08 ,  G06N3/0454 ,  G06N3/0472

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributed training of reinforcement learning systems. One of the methods includes receiving, by a learner, current values of the parameters of the Q network from a parameter server, wherein each learner maintains a respective learner Q network replica and a respective target Q network replica; updating, by the learner, the parameters of the learner Q network replica maintained by the learner using the current values; selecting, by the learner, an experience tuple from a respective replay memory; computing, by the learner, a gradient from the experience tuple using the learner Q network replica maintained by the learner and the target Q network replica maintained by the learner; and providing, by the learner, the computed gradient to the parameter server.

    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，用于强化学习系统的分布式训练。 其中一种方法包括从学习者接收来自参数服务器的Q网络参数的当前值，其中每个学习者维护相应的学习者Q网络副本和相应的目标Q网络副本; 由学习者更新由学习者使用当前值维护的学习者Q网络副本的参数; 由学习者选择来自相应回放记忆的经验元组; 由学习者使用由学习者维护的学习者Q网络副本和学习者维护的目标Q网络副本的经验元组进行计算; 并且由学习者将计算的梯度提供给参数服务器。

    PROGRESSIVE NEURAL NETWORKS
    6.
    发明申请
    PROGRESSIVE NEURAL NETWORKS 审中-公开

    公开(公告)号：US20170337464A1

    公开(公告)日：2017-11-23

    申请号：US15396319

    申请日：2016-12-30

    Applicant: Google Inc.

    Inventor： Neil Charles Rabinowitz ,  Guillaume Desjardins ,  Andrei-Alexandru Rusu ,  Koray Kavukcuoglu ,  Raia Thais Hadsell ,  Razvan Pascanu ,  James Kirkpatrick ,  Hubert Josef Soyer

    IPC: G06N3/04 ,  G06N3/08 ,  G06F17/16

    CPC classification number: G06F17/16 ,  G06N3/0454

    Abstract: Methods and systems for performing a sequence of machine learning tasks. One system includes a sequence of deep neural networks (DNNs), including: a first DNN corresponding to a first machine learning task, wherein the first DNN comprises a first plurality of indexed layers, and each layer in the first plurality of indexed layers is configured to receive a respective layer input and process the layer input to generate a respective layer output; and one or more subsequent DNNs corresponding to one or more respective machine learning tasks, wherein each subsequent DNN comprises a respective plurality of indexed layers, and each layer in a respective plurality of indexed layers with index greater than one receives input from a preceding layer of the respective subsequent DNN, and one or more preceding layers of respective preceding DNNs, wherein a preceding layer is a layer whose index is one less than the current index.

    SPATIAL TRANSFORMER MODULES
    7.
    发明申请
    SPATIAL TRANSFORMER MODULES 审中-公开
    空间变压器模块

    公开(公告)号：US20160358038A1

    公开(公告)日：2016-12-08

    申请号：US15174133

    申请日：2016-06-06

    Applicant: Google Inc.

    Inventor： Maxwell Elliot Jaderberg ,  Karen Simonyan ,  Andrew Zisserman ,  Koray Kavukcuoglu

    IPC: G06K9/52 ,  G06N3/08 ,  G06K9/03

    CPC classification number: G06K9/527 ,  G06K9/03 ,  G06K9/4628 ,  G06N3/0454 ,  G06N3/084 ,  G06N3/088

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using an image processing neural network system that includes a spatial transformer module. One of the methods includes receiving an input feature map derived from the one or more input images, and applying a spatial transformation to the input feature map to generate a transformed feature map, comprising: processing the input feature map to generate spatial transformation parameters for the spatial transformation, and sampling from the input feature map in accordance with the spatial transformation parameters to generate the transformed feature map.

    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，用于使用包括空间变换器模块的图像处理神经网络系统处理输入。 其中一种方法包括接收从一个或多个输入图像导出的输入特征图，以及将空间变换应用于输入特征图以产生变换后的特征图，包括：处理输入特征图以产生空间变换参数 空间变换，并根据空间变换参数从输入特征图进行采样，生成变换后的特征图。

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