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    REINFORCEMENT LEARNING WITH AUXILIARY TASKS
    43.
    发明申请
    REINFORCEMENT LEARNING WITH AUXILIARY TASKS 审中-公开

    公开(公告)号：US20190258938A1

    公开(公告)日：2019-08-22

    申请号：US16403385

    申请日：2019-05-03

    Applicant: DeepMind Technologies Limited

    Inventor： Volodymyr Mnih ,  Wojciech Czarnecki ,  Maxwell Elliot Jaderberg ,  Tom Schaul ,  David Silver ,  Koray Kavukcuoglu

    IPC: G06N3/08 ,  G06N20/00

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a reinforcement learning system. The method includes: training an action selection policy neural network, and during the training of the action selection neural network, training one or more auxiliary control neural networks and a reward prediction neural network. Each of the auxiliary control neural networks is configured to receive a respective intermediate output generated by the action selection policy neural network and generate a policy output for a corresponding auxiliary control task. The reward prediction neural network is configured to receive one or more intermediate outputs generated by the action selection policy neural network and generate a corresponding predicted reward. Training each of the auxiliary control neural networks and the reward prediction neural network comprises adjusting values of the respective auxiliary control parameters, reward prediction parameters, and the action selection policy network parameters.

    SPATIAL TRANSFORMER MODULES
    45.
    发明申请
    SPATIAL TRANSFORMER MODULES 审中-公开

    公开(公告)号：US20180330185A1

    公开(公告)日：2018-11-15

    申请号：US16041567

    申请日：2018-07-20

    Applicant: DeepMind Technologies Limited

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

    IPC: G06K9/52 ,  G06N3/08 ,  G06K9/46 ,  G06K9/03 ,  G06N3/04

    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.

    Using Hierarchical Representations for Neural Network Architecture Searching
    46.
    发明公开
    Using Hierarchical Representations for Neural Network Architecture Searching 审中-公开

    公开(公告)号：US20240249146A1

    公开(公告)日：2024-07-25

    申请号：US18415376

    申请日：2024-01-17

    Applicant: DeepMind Technologies Limited

    Inventor： Chrisantha Thomas Fernando ,  Karen Simonyan ,  Koray Kavukcuoglu ,  Hanxiao Liu ,  Oriol Vinyals

    IPC: G06N3/086 ,  G06F16/901 ,  G06F17/15 ,  G06N3/045

    CPC classification number: G06N3/086 ,  G06F16/9024 ,  G06N3/045 ,  G06F17/15

    Abstract: A computer-implemented method for automatically determining a neural network architecture represents a neural network architecture as a data structure defining a hierarchical set of directed acyclic graphs in multiple levels. Each graph has an input, an output, and a plurality of nodes between the input and the output. At each level, a corresponding set of the nodes are connected pairwise by directed edges which indicate operations performed on outputs of one node to generate an input to another node. Each level is associated with a corresponding set of operations. At a lowest level, the operations associated with each edge are selected from a set of primitive operations. The method includes repeatedly generating new sample neural network architectures, and evaluating their fitness. The modification is performed by selecting a level, selecting two nodes at that level, and modifying, removing or adding an edge between those nodes according to operations associated with lower levels of the hierarchy.

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