METHODS AND APPARATUS FOR TAGGING CLASSES USING SUPERVISED LEARNING
    4.
    发明申请
    METHODS AND APPARATUS FOR TAGGING CLASSES USING SUPERVISED LEARNING 有权
    使用监督学习标签类的方法和装置

    公开(公告)号:US20150120626A1

    公开(公告)日:2015-04-30

    申请号:US14065089

    申请日:2013-10-28

    CPC classification number: G06N3/049 G06N3/0454 G06N3/08

    Abstract: Certain aspects of the present disclosure provide methods and apparatus for creating tags (static or dynamic) for input/output classes of a neural network model using supervised learning. The method includes augmenting a neural network model with a plurality of neurons and training the augmented network using spike timing dependent plasticity (STDP) to determine one or more tags.

    Abstract translation: 本公开的某些方面提供了使用监督学习为神经网络模型的输入/输出类创建标签(静态或动态)的方法和装置。 该方法包括用多个神经元增强神经网络模型,并使用尖峰定时相关可塑性(STDP)训练增强网络来确定一个或多个标签。

    CONTENT ADAPTATION SYSTEM
    5.
    发明申请

    公开(公告)号:US20250054212A1

    公开(公告)日:2025-02-13

    申请号:US18447858

    申请日:2023-08-10

    Abstract: Systems and techniques are provided for adapting digital content. For example, a process can include obtaining a digital content comprising a default configuration for outputting the digital content to a device; outputting, by the device, the digital content based on the default configuration for outputting the digital content. The process can include obtaining, from a monitoring engine, content interaction information associated with outputting, by the device, the digital content based on the default configuration for outputting the digital content. The monitoring engine is configured to monitor one or more interactions between one or more users of the device and the digital content. The process can include generating, based on the content interaction information, a content adaptation for the digital content. The process can include outputting, by the device, the content adaptation for the digital content.

    NEURONAL DIVERSITY IN SPIKING NEURAL NETWORKS AND PATTERN CLASSIFICATION
    6.
    发明申请
    NEURONAL DIVERSITY IN SPIKING NEURAL NETWORKS AND PATTERN CLASSIFICATION 审中-公开
    在神经网络和模式分类中的神经元多样性

    公开(公告)号:US20150170028A1

    公开(公告)日:2015-06-18

    申请号:US14526317

    申请日:2014-10-28

    CPC classification number: G06N3/08 G06N3/049

    Abstract: A method for pattern recognition in a spiking neural network robust to initial network conditions includes creating a set of diverse neurons in a first layer to increase a diversity in a set of spike timings. An input corresponding to a pattern plus noise is presented at an input layer and represented as spikes. The spikes are received at the first layer and spikes are produced at the first layer based on the received spikes. The method also includes updating a weight of each synapse between an input layer neuron and an output layer neuron based on a spike timing difference between a spike at the input layer neuron and a spike at the output layer neuron. Further, the method includes classifying a spike pattern represented by a set of inter-spike intervals, regardless of noise in the spike pattern.

    Abstract translation: 在对初始网络条件稳健的加标神经网络中的模式识别的方法包括在第一层中创建一组不同的神经元以增加一组尖峰定时的分集。 对应于图案加噪声的输入在输入层处呈现并表示为尖峰。 尖峰在第一层被接收,并且基于接收的尖峰在第一层产生尖峰。 该方法还包括基于输入层神经元的尖峰与输出层神经元的尖峰之间的尖峰定时差来更新输入层神经元和输出层神经元之间的每个突触的权重。 此外,该方法包括分类由一组间穗间隔表示的尖峰图案,而不管尖峰图案中的噪声如何。

    MULTI-USER EXPERIENCE COORDINATION SYSTEM

    公开(公告)号:US20250055717A1

    公开(公告)日:2025-02-13

    申请号:US18447939

    申请日:2023-08-10

    Abstract: Systems and techniques are provided for coordinating multi-user experiences. For example, a process can include obtaining a plurality of settings associated with a plurality of multi-user experience participants. The plurality of settings includes one or more arbitrated settings and one or more non-arbitrated settings. The process can include arbitrating, by a settings arbitration engine, the one or more arbitrated settings to generate one or more adjusted settings for each arbitrated setting. The process can include generating, by an experience adaptation engine, an adapted multi-user experience, wherein the adapted multi-user experience is configured to enforce the one or more adjusted settings for each arbitrated setting.

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