中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

100 records (took 0.024 seconds)

    Neural machine translation systems
    92.
    发明授权
    Neural machine translation systems 有权

    公开(公告)号：US11113480B2

    公开(公告)日：2021-09-07

    申请号：US16336870

    申请日：2017-09-25

    Applicant: GOOGLE LLC

    Inventor： Mohammad Norouzi ,  Zhifeng Chen ,  Yonghui Wu ,  Michael Schuster ,  Quoc V. Le

    IPC: G06F40/58 ,  G06F40/44 ,  G06N3/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for neural machine translation. One of the systems includes an encoder neural network comprising: an input forward long short-term memory (LSTM) layer configured to process each input token in the input sequence in a forward order to generate a respective forward representation of each input token, an input backward LSTM layer configured to process each input token in a backward order to generate a respective backward representation of each input token and a plurality of hidden LSTM layers configured to process a respective combined representation of each of the input tokens in the forward order to generate a respective encoded representation of each of the input tokens; and a decoder subsystem configured to receive the respective encoded representations and to process the encoded representations to generate an output sequence.

    MINIMUM WORD ERROR RATE TRAINING FOR ATTENTION-BASED SEQUENCE-TO-SEQUENCE MODELS
    94.
    发明申请
    MINIMUM WORD ERROR RATE TRAINING FOR ATTENTION-BASED SEQUENCE-TO-SEQUENCE MODELS 审中-公开

    公开(公告)号：US20200043483A1

    公开(公告)日：2020-02-06

    申请号：US16529252

    申请日：2019-08-01

    Applicant: Google LLC

    Inventor： Rohit Prakash Prabhavalkar ,  Tara N. Sainath ,  Yonghui Wu ,  Patrick An Phu Nguyen ,  Zhifeng Chen ,  Chung-Cheng Chiu ,  Anjuli Patricia Kannan

    IPC: G10L15/197 ,  G10L15/16 ,  G10L15/22 ,  G10L15/06 ,  G10L15/02

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-readable storage media, for speech recognition using attention-based sequence-to-sequence models. In some implementations, audio data indicating acoustic characteristics of an utterance is received. A sequence of feature vectors indicative of the acoustic characteristics of the utterance is generated. The sequence of feature vectors is processed using a speech recognition model that has been trained using a loss function that uses N-best lists of decoded hypotheses, the speech recognition model including an encoder, an attention module, and a decoder. The encoder and decoder each include one or more recurrent neural network layers. A sequence of output vectors representing distributions over a predetermined set of linguistic units is obtained. A transcription for the utterance is obtained based on the sequence of output vectors. Data indicating the transcription of the utterance is provided.

    NEURAL MACHINE TRANSLATION SYSTEMS
    95.
    发明申请
    NEURAL MACHINE TRANSLATION SYSTEMS 审中-公开

    公开(公告)号：US20200034435A1

    公开(公告)日：2020-01-30

    申请号：US16336870

    申请日：2017-09-25

    Applicant: GOOGLE LLC

    Inventor： Mohammad Norouzi ,  Zhifeng Chen ,  Yonghui Wu ,  Michael Schuster ,  Quoc V. Le

    IPC: G06F17/28 ,  G06N3/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for neural machine translation. One of the systems includes an encoder neural network comprising: an input forward long short-term memory (LSTM) layer configured to process each input token in the input sequence in a forward order to generate a respective forward representation of each input token, an input backward LSTM layer configured to process each input token in a backward order to generate a respective backward representation of each input token and a plurality of hidden LSTM layers configured to process a respective combined representation of each of the input tokens in the forward order to generate a respective encoded representation of each of the input tokens; and a decoder subsystem configured to receive the respective encoded representations and to process the encoded representations to generate an output sequence.

    IMPLICIT BRIDGING OF MACHINE LEARNING TASKS
    98.
    发明申请
    IMPLICIT BRIDGING OF MACHINE LEARNING TASKS 审中-公开

    公开(公告)号：US20190258961A1

    公开(公告)日：2019-08-22

    申请号：US16402787

    申请日：2019-05-03

    Applicant: Google LLC

    Inventor： Zhifeng Chen ,  Michael Schuster ,  Melvin Jose Johnson Premkumar ,  Yonghui Wu ,  Quoc V. Le ,  Maxim Krikun ,  Thorsten Brants

    IPC: G06N20/00 ,  G06N3/04 ,  G06F17/28

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for performing machine learning tasks. One method includes receiving (i) a model input, and (ii) data identifying a first machine learning task to be performed on the model input to generate a first type of model output for the model input; augmenting the model input with an identifier for the first machine learning task to generate an augmented model input; and processing the augmented model input using a machine learning model. An exemplary system applying implicit bridging for machine learning tasks, as described in this specification, trains a machine learning model to perform certain types of machine learning tasks without requiring explicit training data for the certain types of machine learning tasks to be used during training.

    Generating descriptive text for images in documents using seed descriptors
    99.
    发明授权
    Generating descriptive text for images in documents using seed descriptors 有权

    公开(公告)号：US10248662B2

    公开(公告)日：2019-04-02

    申请号：US15926726

    申请日：2018-03-20

    Applicant: Google LLC

    Inventor： Yonghui Wu ,  Michael E. Flaster ,  Randall G. Keller ,  Paul Haahr

    IPC: H03M5/00 ,  H03M7/00 ,  G06F17/30 ,  G06F17/27 ,  G06F17/21 ,  G06F3/0484

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating descriptive text for images. In one aspect, a method includes identifying a set of seed descriptors for an image in a document that is hosted on a website. For each seed descriptor, structure information is generated that specifies a structure of the document with respect to the image and the seed descriptor. One or more templates are generated for each seed descriptor using the structure information for the seed descriptor. Each template can include image location information, document structure information, image feature information, and a generative rule that generates descriptive text for other images in other documents. Descriptive text for other images is generated using the templates and the other documents. The descriptive text is associated with the images.

Patent Agency Ranking