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    • 6. 发明授权
    • Computer-implemented deep tensor neural network
    • 计算机实现的深张量神经网络
    • US09292787B2
    • 2016-03-22
    • US13597268
    • 2012-08-29
    • Dong YuLi DengFrank Seide
    • Dong YuLi DengFrank Seide
    • G06N3/02G06F15/18G06N3/04
    • G06N3/02G06N3/04G06N3/0454G06N3/084
    • A deep tensor neural network (DTNN) is described herein, wherein the DTNN is suitable for employment in a computer-implemented recognition/classification system. Hidden layers in the DTNN comprise at least one projection layer, which includes a first subspace of hidden units and a second subspace of hidden units. The first subspace of hidden units receives a first nonlinear projection of input data to a projection layer and generates the first set of output data based at least in part thereon, and the second subspace of hidden units receives a second nonlinear projection of the input data to the projection layer and generates the second set of output data based at least in part thereon. A tensor layer, which can converted into a conventional layer of a DNN, generates the third set of output data based upon the first set of output data and the second set of output data.
    • 本文描述了深张量神经网络(DTNN),其中DTNN适合于在计算机实现的识别/分类系统中的使用。 DTNN中的隐藏层包括至少一个投影层,其包括隐藏单元的第一子空间和隐藏单元的第二子空间。 隐藏单元的第一子空间至少部分地将输入数据的第一非线性投影接收到投影层,并且至少部分地生成第一组输出数据,并且隐藏单元的第二子空间接收输入数据的第二非线性投影 投影层并且至少部分地基于其生成第二组输出数据。 可以转换成DNN的常规层的张量层基于第一组输出数据和第二组输出数据产生第三组输出数据。
    • 8. 发明申请
    • COMPUTER-IMPLEMENTED DEEP TENSOR NEURAL NETWORK
    • 计算机实现深度传感器神经网络
    • US20140067735A1
    • 2014-03-06
    • US13597268
    • 2012-08-29
    • Dong YuLi DengFrank Seide
    • Dong YuLi DengFrank Seide
    • G06N3/08
    • G06N3/02G06N3/04G06N3/0454G06N3/084
    • A deep tensor neural network (DTNN) is described herein, wherein the DTNN is suitable for employment in a computer-implemented recognition/classification system. Hidden layers in the DTNN comprise at least one projection layer, which includes a first subspace of hidden units and a second subspace of hidden units. The first subspace of hidden units receives a first nonlinear projection of input data to a projection layer and generates the first set of output data based at least in part thereon, and the second subspace of hidden units receives a second nonlinear projection of the input data to the projection layer and generates the second set of output data based at least in part thereon. A tensor layer, which can converted into a conventional layer of a DNN, generates the third set of output data based upon the first set of output data and the second set of output data.
    • 本文描述了深张量神经网络(DTNN),其中DTNN适合于在计算机实现的识别/分类系统中的使用。 DTNN中的隐藏层包括至少一个投影层,其包括隐藏单元的第一子空间和隐藏单元的第二子空间。 隐藏单元的第一子空间至少部分地将输入数据的第一非线性投影接收到投影层,并且至少部分地生成第一组输出数据,并且隐藏单元的第二子空间接收输入数据的第二非线性投影 投影层并且至少部分地基于其生成第二组输出数据。 可以转换成DNN的常规层的张量层基于第一组输出数据和第二组输出数据产生第三组输出数据。
    • 9. 发明申请
    • DISTRIBUTED INDEXING OF FILE CONTENT
    • 分配的文件内容的索引
    • US20090187588A1
    • 2009-07-23
    • US12018203
    • 2008-01-23
    • Albert J. K. ThambiratnamFrank Seide
    • Albert J. K. ThambiratnamFrank Seide
    • G06F17/30
    • G06F16/134
    • Described herein is technology for, among other things, distributed indexing of file content. Content-based indexing the file involves determining whether content-based index information for the file is available from an external source. This avoids repeating already-performed content analysis, which is time consuming and computationally intensive especially for non-text files. The content-based index information, if it is available, is received from the external source and may be stored. If the content-based index information is not available or is not complete, content-based index information for the file is generated and stored. Moreover, the generated content-based index information is shared with the external source. Once content analysis of the file is performed to generate content-based index information for the file, the content-based index information is available and sharable as needed. There is no need to repeat the same content analysis on the file.
    • 这里描述的是用于文件内容的分布式索引的技术。 基于内容的索引文件涉及确定文件的基于内容的索引信息是否可从外部来源获得。 这避免了重复已经执行的内容分析,这对于非文本文件来说是耗时且计算密集的。 基于内容的索引信息(如果可用)从外部源接收并且可以被存储。 如果基于内容的索引信息不可用或不完整,则生成并存储该文件的基于内容的索引信息。 此外,生成的基于内容的索引信息与外部源共享。 一旦文件的内容分析被执行以产生用于文件的基于内容的索引信息,则基于内容的索引信息是可用的并且根据需要可共享。 没有必要对文件重复相同的内容分析。