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    PARALLEL CASCADED NEURAL NETWORKS
    32.
    发明申请
    PARALLEL CASCADED NEURAL NETWORKS 有权

    公开(公告)号：US20220253695A1

    公开(公告)日：2022-08-11

    申请号：US17560139

    申请日：2021-12-22

    Applicant: Google LLC

    Inventor： Michael Curtis Mozer ,  Michael Louis Iuzzolino ,  Samuel Bengio

    IPC: G06N3/08

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing a network input using a parallel cascaded neural network that includes multiple neural network blocks that each have a skip connection and a propagation delay. Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training parallel cascaded neural networks using temporal difference learning are also described.

    Processing and generating sets using recurrent neural networks
    33.
    发明授权
    Processing and generating sets using recurrent neural networks 有权

    公开(公告)号：US11263514B2

    公开(公告)日：2022-03-01

    申请号：US15406557

    申请日：2017-01-13

    Applicant: Google LLC

    Inventor： Oriol Vinyals ,  Samuel Bengio

    IPC: G06N3/04

    Abstract: In one aspect, this specification describes a recurrent neural network system implemented by one or more computers that is configured to process input sets to generate neural network outputs for each input set. The input set can be a collection of multiple inputs for which the recurrent neural network should generate the same neural network output regardless of the order in which the inputs are arranged in the collection. The recurrent neural network system can include a read neural network, a process neural network, and a write neural network. In another aspect, this specification describes a system implemented as computer programs on one or more computers in one or more locations that is configured to train a recurrent neural network that receives a neural network input and sequentially emits outputs to generate an output sequence for the neural network input.

    Neural networks for speaker verification
    34.
    发明授权
    Neural networks for speaker verification 有权

    公开(公告)号：US11107478B2

    公开(公告)日：2021-08-31

    申请号：US16752007

    申请日：2020-01-24

    Applicant: Google LLC

    Inventor： Georg Heigold ,  Samuel Bengio ,  Ignacio Lopez Moreno

    IPC: G10L17/18 ,  G10L17/04 ,  G10L17/02

    Abstract: This document generally describes systems, methods, devices, and other techniques related to speaker verification, including (i) training a neural network for a speaker verification model, (ii) enrolling users at a client device, and (iii) verifying identities of users based on characteristics of the users' voices. Some implementations include a computer-implemented method. The method can include receiving, at a computing device, data that characterizes an utterance of a user of the computing device. A speaker representation can be generated, at the computing device, for the utterance using a neural network on the computing device. The neural network can be trained based on a plurality of training samples that each: (i) include data that characterizes a first utterance and data that characterizes one or more second utterances, and (ii) are labeled as a matching speakers sample or a non-matching speakers sample.

    Generating Natural Language Descriptions of Images
    36.
    发明申请
    Generating Natural Language Descriptions of Images 有权

    公开(公告)号：US20210125038A1

    公开(公告)日：2021-04-29

    申请号：US17092837

    申请日：2020-11-09

    Applicant: Google LLC

    Inventor： Samuel Bengio ,  Oriol Vinyals ,  Alexander Toshkov Toshev ,  Dumitru Erhan

    IPC: G06N3/04 ,  G06F40/40

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating descriptions of input images. One of the methods includes obtaining an input image; processing the input image using a first neural network to generate an alternative representation for the input image; and processing the alternative representation for the input image using a second neural network to generate a sequence of a plurality of words in a target natural language that describes the input image.

    DEVICE PLACEMENT OPTIMIZATION WITH REINFORCEMENT LEARNING
    37.
    发明申请
    DEVICE PLACEMENT OPTIMIZATION WITH REINFORCEMENT LEARNING 审中-公开

    公开(公告)号：US20200279163A1

    公开(公告)日：2020-09-03

    申请号：US16878720

    申请日：2020-05-20

    Applicant: Google LLC

    Inventor： Samuel Bengio ,  Mohammad Norouzi ,  Benoit Steiner ,  Jeffrey Adgate Dean ,  Hieu Hy Pham ,  Azalia Mirhoseini ,  Quoc V. Le ,  Naveen Kumar ,  Yuefeng Zhou ,  Rasmus Munk Larsen

    IPC: G06N3/08 ,  G06N5/04 ,  G06N3/04 ,  G06N3/10

    Abstract: A method for determining a placement for machine learning model operations across multiple hardware devices is described. The method includes receiving data specifying a machine learning model to be placed for distributed processing on multiple hardware devices; generating, from the data, a sequence of operation embeddings, each operation embedding in the sequence characterizing respective operations necessary to perform the processing of the machine learning model; processing the sequence of operation embeddings using a placement recurrent neural network in accordance with first values of a plurality network parameters of the placement recurrent neural network to generate a network output that defines a placement of the operations characterized by the operation embeddings in the sequence across the plurality of devices; and scheduling the machine learning model for processing by the multiple hardware devices by placing the operations on the multiple devices according to the placement defined by the network output.

    Neural networks for speaker verification
    38.
    发明授权
    Neural networks for speaker verification 有权

    公开(公告)号：US10586542B2

    公开(公告)日：2020-03-10

    申请号：US15966667

    申请日：2018-04-30

    Applicant: Google LLC

    Inventor： Georg Heigold ,  Samuel Bengio ,  Ignacio Lopez Moreno

    IPC: G10L17/18 ,  G10L17/04 ,  G10L17/02

    Abstract: This document generally describes systems, methods, devices, and other techniques related to speaker verification, including (i) training a neural network for a speaker verification model, (ii) enrolling users at a client device, and (iii) verifying identities of users based on characteristics of the users' voices. Some implementations include a computer-implemented method. The method can include receiving, at a computing device, data that characterizes an utterance of a user of the computing device. A speaker representation can be generated, at the computing device, for the utterance using a neural network on the computing device. The neural network can be trained based on a plurality of training samples that each: (i) include data that characterizes a first utterance and data that characterizes one or more second utterances, and (ii) are labeled as a matching speakers sample or a non-matching speakers sample.

    REWARD AUGMENTED MODEL TRAINING
    40.
    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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