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    Training recurrent neural networks to generate sequences
    43.
    发明授权
    Training recurrent neural networks to generate sequences 有权

    公开(公告)号：US11003993B1

    公开(公告)日：2021-05-11

    申请号：US16707464

    申请日：2019-12-09

    Applicant: Google LLC

    Inventor： Samy Bengio ,  Oriol Vinyals ,  Navdeep Jaitly ,  Noam M. Shazeer

    IPC: G06N3/08

    Abstract: This document generally describes a neural network training system, including one or more computers, that trains a recurrent neural network (RNN) to receive an input, e.g., an input sequence, and to generate a sequence of outputs from the input sequence. In some implementations, training can include, for each position after an initial position in a training target sequence, selecting a preceding output of the RNN to provide as input to the RNN at the position, including determining whether to select as the preceding output (i) a true output in a preceding position in the output order or (ii) a value derived from an output of the RNN for the preceding position in an output order generated in accordance with current values of the parameters of the recurrent neural network.

    VERY DEEP CONVOLUTIONAL NEURAL NETWORKS FOR END-TO-END SPEECH RECOGNITION
    45.
    发明申请
    VERY DEEP CONVOLUTIONAL NEURAL NETWORKS FOR END-TO-END SPEECH RECOGNITION 审中-公开

    公开(公告)号：US20200090044A1

    公开(公告)日：2020-03-19

    申请号：US16692538

    申请日：2019-11-22

    Applicant: Google LLC

    Inventor： Navdeep Jaitly ,  Yu Zhang ,  William Chan

    IPC: G06N3/08 ,  G06N3/04 ,  G10L15/16 ,  G10L15/02 ,  G10L15/22

    Abstract: A speech recognition neural network system includes an encoder neural network and a decoder neural network. The encoder neural network generates an encoded sequence from an input acoustic sequence that represents an utterance. The input acoustic sequence includes a respective acoustic feature representation at each of a plurality of input time steps, the encoded sequence includes a respective encoded representation at each of a plurality of time reduced time steps, and the number of time reduced time steps is less than the number of input time steps. The encoder neural network includes a time reduction subnetwork, a convolutional LSTM subnetwork, and a network in network subnetwork. The decoder neural network receives the encoded sequence and processes the encoded sequence to generate, for each position in an output sequence order, a set of sub string scores that includes a respective sub string score for each substring in a set of substrings.

    Generating output sequences from input sequences using neural networks
    47.
    发明授权
    Generating output sequences from input sequences using neural networks 有权

    公开(公告)号：US10402719B1

    公开(公告)日：2019-09-03

    申请号：US15076426

    申请日：2016-03-21

    Applicant: Google LLC

    Inventor： Oriol Vinyals ,  Navdeep Jaitly

    IPC: G06N3/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating output sequences from input sequences. One of the methods includes obtaining an input sequence having a first number of inputs arranged according to an input order; processing each input in the input sequence using an encoder recurrent neural network to generate a respective encoder hidden state for each input in the input sequence; and generating an output sequence having a second number of outputs arranged according to an output order, each output in the output sequence being selected from the inputs in the input sequence, comprising, for each position in the output order: generating a softmax output for the position using the encoder hidden states that is a pointer into the input sequence; and selecting an input from the input sequence as the output at the position using the softmax output.

    REWARD AUGMENTED MODEL TRAINING
    48.
    发明申请
    REWARD AUGMENTED MODEL TRAINING 审中-公开

    公开(公告)号：US20190188566A1

    公开(公告)日：2019-06-20

    申请号：US16328207

    申请日：2017-08-25

    Applicant: GOOGLE LLC

    Inventor： Michael Schuster ,  Samuel Bengio ,  Navdeep Jaitly ,  Zhifeng Chen ,  Dale Eric Schuurmans ,  Mohammad Norouzi ,  Yonghui Wu

    IPC: G06N3/08 ,  G06N20/00

    CPC classification number: G06N3/08 ,  G06N20/00

    Abstract: A method includes obtaining data identifying a machine learning model to be trained to perform a machine learning task, the machine learning model being configured to receive an input example and to process the input example in accordance with current values of a plurality of model parameters to generate a model output for the input example; obtaining initial training data for training the machine learning model, the initial training data comprising a plurality of training examples and, for each training example, a ground truth output that should be generated by the machine learning model by processing the training example; generating modified training data from the initial training data; and training the machine learning model on the modified training data.

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