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    METHOD FOR IMPROVING PERFORMANCE OF A TRAINED MACHINE LEARNING MODEL
    1.
    发明申请
    METHOD FOR IMPROVING PERFORMANCE OF A TRAINED MACHINE LEARNING MODEL 审中-公开
    改进训练机器学习模型性能的方法

    公开(公告)号：US20170061326A1

    公开(公告)日：2017-03-02

    申请号：US14863410

    申请日：2015-09-23

    Applicant: QUALCOMM Incorporated

    Inventor： Sachin Subhash TALATHI ,  Aniket VARTAK

    IPC: G06N99/00

    CPC classification number: G06N20/00 ,  G06K9/4628 ,  G06K9/6257 ,  G06K9/6267 ,  G06K9/627 ,  G06N3/02 ,  G06N3/04 ,  G06N3/082

    Abstract: A method for improving performance of a trained machine learning model includes adding a second classifier with a second objective function to a first classifier with a first objective function. Rather than minimizing a function of errors for the first classifier, the second objective function is used to directly reduce the number errors of the first classifier.

    Abstract translation: 一种用于改善经过训练的机器学习模型的性能的方法包括：向具有第一目标函数的第一分类器添加具有第二目标函数的第二分类器。 不是将第一分类器的误差函数最小化，而是使用第二目标函数来直接减少第一分类器的数量误差。

    CONFIGURING SPARSE NEURONAL NETWORKS
    2.
    发明申请
    CONFIGURING SPARSE NEURONAL NETWORKS 审中-公开
    配置稀疏神经网络

    公开(公告)号：US20150206048A1

    公开(公告)日：2015-07-23

    申请号：US14449101

    申请日：2014-07-31

    Applicant: QUALCOMM Incorporated

    Inventor： Sachin Subhash TALATHI ,  David Jonathan JULIAN ,  Venkata Sreekanta Reddy ANNAPUREDDY

    IPC: G06N3/04

    CPC classification number: G06N3/08 ,  G06N3/0481 ,  G06N3/049 ,  G06N3/082

    Abstract: A method for selecting a reduced number of model neurons in a neural network includes generating a first sparse set of non-zero decoding vectors. Each of the decoding vector is associated with a synapse between a first neuron layer and a second neuron layer. The method further includes implementing the neural network only with selected model neurons in the first neuron layer associated with the non-zero decoding vectors.

    Abstract translation: 用于在神经网络中选择减少数量的模型神经元的方法包括生成非零解码矢量的第一稀疏集合。 每个解码矢量与第一神经元层和第二神经元层之间的突触相关联。 该方法还包括仅在与非零解码矢量相关联的第一神经元层中实施神经网络与选定的模型神经元。

    HYPER-PARAMETER SELECTION FOR DEEP CONVOLUTIONAL NETWORKS
    3.
    发明申请
    HYPER-PARAMETER SELECTION FOR DEEP CONVOLUTIONAL NETWORKS 审中-公开
    深层调节网络的参数选择

    公开(公告)号：US20160224903A1

    公开(公告)日：2016-08-04

    申请号：US14848296

    申请日：2015-09-08

    Applicant: QUALCOMM Incorporated

    Inventor： Sachin Subhash TALATHI ,  David Jonathan JULIAN

    IPC: G06N99/00 ,  G06N3/08

    CPC classification number: G06N99/005 ,  G06N3/08 ,  G06N3/082 ,  G06N7/005

    Abstract: Hyper-parameters are selected for training a deep convolutional network by selecting a number of network architectures as part of a database. Each of the network architectures includes one or more local logistic regression layer and is trained to generate a corresponding validation error that is stored in the database. A threshold error for identifying a good set of network architectures and a bad set of network architectures may be estimated based on validation errors in the database. The method also includes choosing a next potential hyper-parameter, corresponding to a next network architecture, based on a metric that is a function of the good set of network architectures. The method further includes selecting a network architecture, from among next network architectures, with a lowest validation error.

    Abstract translation: 选择超参数以通过选择多个网络架构作为数据库的一部分来训练深卷积网络。 每个网络架构包括一个或多个本地逻辑回归层，并被训练以产生存储在数据库中的对应验证错误。 可以基于数据库中的验证错误来估计用于识别良好的一组网络架构和一组坏的网络架构的阈值误差。 该方法还包括基于作为良好网络体系结构的函数的度量来选择对应于下一个网络体系结构的下一个潜在的超参数。 该方法还包括从下一个网络体系结构中选择具有最低验证错误的网络架构。

    NEURAL NETWORK FOR IMAGE PROCESSING
    7.
    发明申请
    NEURAL NETWORK FOR IMAGE PROCESSING 审中-公开

    公开(公告)号：US20180101957A1

    公开(公告)日：2018-04-12

    申请号：US15422395

    申请日：2017-02-01

    Applicant: QUALCOMM Incorporated

    Inventor： Sachin Subhash TALATHI

    IPC: G06T7/10 ,  G06T5/00 ,  G06T3/40 ,  G06K9/46

    CPC classification number: G06T7/10 ,  G06K9/46 ,  G06T3/4053 ,  G06T5/002 ,  G06T5/003 ,  G06T9/00 ,  G06T2207/10024 ,  G06T2207/20081 ,  G06T2207/20084

    Abstract: A method for processing an input in an artificial neural network (ANN) includes receiving, at an operator layer of a set of operator layers, a first feature value based on the input from a decoder convolutional layer of a decoder. The operator layer also receives a second feature value based on the input from an encoder convolutional layer of a encoder. The method also includes determining, at the operator layer, a third feature value based on the input by performing an element-wise operation with the first feature value based on the input and the second feature value based on the input. The method transmits, from the operator layer, the third feature value based on the input to an encoder layer that is subsequent to the encoder convolutional layer. The method generates an output based on the third feature value based on the input.

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