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    PAIRWISE RANKING USING NEURAL NETWORKS
    83.
    发明申请
    PAIRWISE RANKING USING NEURAL NETWORKS 有权

    公开(公告)号：US20210406680A1

    公开(公告)日：2021-12-30

    申请号：US16951362

    申请日：2020-11-18

    Applicant: DeepMind Technologies Limited

    Inventor： Xiaohong Gong ,  Arturo Bajuelos Castillo ,  Sanjeev Jagannatha Rao ,  Xueliang Lu ,  Amogh S. Asgekar ,  Anton Alexandrov ,  Carsten Miklos Steinebach

    IPC: G06N3/08 ,  G06N3/04 ,  G06Q30/06

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network used to generate a ranking score for a network input. One of the methods includes generating training data and training the neural network on the training data. The training data includes a plurality of training pairs. The generating comprising: obtaining data indicating that a plurality of training network inputs were displayed in a user interface according to a presentation order, obtaining data indicating that a first training network input of the plurality of training network inputs has a positive label, determining that a second training network input of the plurality of training network inputs (i) has a negative label and (ii) is higher than the first training network input in the presentation order, and generating a training pair that includes the first training network input and the second training network input.

    TRAINING NEURAL NETWORKS USING NORMALIZED TARGET OUTPUTS
    84.
    发明申请
    TRAINING NEURAL NETWORKS USING NORMALIZED TARGET OUTPUTS 有权

    公开(公告)号：US20210319316A1

    公开(公告)日：2021-10-14

    申请号：US17356935

    申请日：2021-06-24

    Applicant: DeepMind Technologies Limited

    Inventor： Hado Philip van Hasselt

    IPC: G06N3/08 ,  G06N3/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network using normalized target outputs. One of the methods includes updating current values of the normalization parameters to account for the target output for the training item; determining a normalized target output for the training item by normalizing the target output for the training item in accordance with the updated normalization parameter values; processing the training item using the neural network to generate a normalized output for the training item in accordance with current values of main parameters of the neural network; determining an error for the training item using the normalized target output and the normalized output; and using the error to adjust the current values of the main parameters of the neural network.

    GENERATIVE NEURAL NETWORK SYSTEMS FOR GENERATING INSTRUCTION SEQUENCES TO CONTROL AN AGENT PERFORMING A TASK
    86.
    发明申请
    GENERATIVE NEURAL NETWORK SYSTEMS FOR GENERATING INSTRUCTION SEQUENCES TO CONTROL AN AGENT PERFORMING A TASK 有权

    公开(公告)号：US20210271968A1

    公开(公告)日：2021-09-02

    申请号：US16967597

    申请日：2019-02-11

    Applicant: DeepMind Technologies Limited

    Inventor： Iaroslav Ganin ,  Tejas Dattatraya Kulkarni ,  Oriol Vinyals ,  Seyed Mohammadali Eslami

    IPC: G06N3/08 ,  G06K9/62 ,  G06F30/20

    Abstract: A generative adversarial neural network system to provide a sequence of actions for performing a task. The system comprises a reinforcement learning neural network subsystem coupled to a simulator and a discriminator neural network. The reinforcement learning neural network subsystem includes a policy recurrent neural network to, at each of a sequence of time steps, select one or more actions to be performed according to an action selection policy, each action comprising one or more control commands for a simulator. The simulator is configured to implement the control commands for the time steps to generate a simulator output. The discriminator neural network is configured to discriminate between the simulator output and training data, to provide a reward signal for the reinforcement learning. The simulator may be non-differentiable simulator, for example a computer program to produce an image or audio waveform or a program to control a robot or vehicle.

    Recurrent neural networks for data item generation
    87.
    发明授权
    Recurrent neural networks for data item generation 有权

    公开(公告)号：US11080587B2

    公开(公告)日：2021-08-03

    申请号：US15016160

    申请日：2016-02-04

    Applicant: DeepMind Technologies Limited

    Inventor： Karol Gregor ,  Ivo Danihelka

    IPC: G06N3/04 ,  G10L25/30 ,  G10L13/02

    Abstract: Methods, and systems, including computer programs encoded on computer storage media for generating data items. A method includes reading a glimpse from a data item using a decoder hidden state vector of a decoder for a preceding time step, providing, as input to a encoder, the glimpse and decoder hidden state vector for the preceding time step for processing, receiving, as output from the encoder, a generated encoder hidden state vector for the time step, generating a decoder input from the generated encoder hidden state vector, providing the decoder input to the decoder for processing, receiving, as output from the decoder, a generated a decoder hidden state vector for the time step, generating a neural network output update from the decoder hidden state vector for the time step, and combining the neural network output update with a current neural network output to generate an updated neural network output.

    Progressive neural networks
    90.
    发明授权
    Progressive neural networks 有权

    公开(公告)号：US10949734B2

    公开(公告)日：2021-03-16

    申请号：US15396319

    申请日：2016-12-30

    Applicant: DeepMind Technologies Limited

    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

    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.

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