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    Deep Neural Networks with No Multiplications and No Floating Point Operations
    21.
    发明申请
    Deep Neural Networks with No Multiplications and No Floating Point Operations 有权

    公开(公告)号：US20210209475A1

    公开(公告)日：2021-07-08

    申请号：US17056549

    申请日：2019-05-20

    Applicant: Google LLC

    Inventor： Shumeet Baluja ,  David Marwood

    IPC: G06N3/08 ,  G06N3/04

    Abstract: The present disclosure provides systems and methods that train and use neural networks that can be run with no multiplications and no floating point operations. In particular, according to one aspect of the present disclosure, the respective non-linear and continuous activation functions typically used by the nodes of a neural network can be replaced with custom activation functions that output one of a discrete number of activation values. Likewise, according to another aspect of the present disclosure, the neural network can be trained such that each of its weights equals one of a discrete number of weight values. Taken together, this enables replacement of the typical multiplication process associated with computing a node of the network with a simple, and much faster, lookup process. In particular, a lookup table can store the result of multiplying each unique pair of activation value and weight value.

    Customized Data Retrieval Applications for Mobile Devices Providing Interpretation of Markup Language Data
    22.
    发明申请
    Customized Data Retrieval Applications for Mobile Devices Providing Interpretation of Markup Language Data 审中-公开

    公开(公告)号：US20200252491A1

    公开(公告)日：2020-08-06

    申请号：US16796930

    申请日：2020-02-20

    Applicant: Google LLC

    Inventor： Elad Gil ,  Shumeet Baluja ,  Maryam Garrett ,  Cedric Beust

    IPC: H04M1/00 ,  H04M1/725 ,  G06F16/248

    Abstract: Systems and techniques, including computer software, for retrieving information to a mobile device involve installing a data retrieval application on the mobile device. The data retrieval application includes instructions for presenting a structured data display on the mobile device, defining a structure of the structured data display, requesting selected hyperlinks included in the structured data display, and rendering markup language information received in response to the selected hyperlinks. A user request to retrieve data through the data retrieval application is received, and data is retrieved in response to the received user request. The retrieved data is displayed according to the structure of the structured data display, and a user can select a hyperlink in the displayed data to retrieve and render markup language information using the data retrieval application.

    Providing content to followers of entity feeds
    24.
    发明授权
    Providing content to followers of entity feeds 有权

    公开(公告)号：US10262029B1

    公开(公告)日：2019-04-16

    申请号：US13894645

    申请日：2013-05-15

    Applicant: Google LLC

    Inventor： Michael Chu ,  Michele Covell ,  Joshua J. Sacks ,  Shumeet Baluja ,  Zhengrong Ji

    IPC: G06F7/00 ,  G06F17/30

    Abstract: Methods, systems and apparatus, including computer programs encoded on a computer storage medium for selecting keywords for resources are disclosed. In one aspect, a search query is received associated with a first user. A determination is made that the first user is a follower of an entity feed that is provided by a first entity and that is provided through a social network. A content item is selected having distribution parameters specifying that the content item is to be provided to users that are followers of the entity feed and that submit the search query. The selected content item is provided for the first user.

    Automatic Language Model Update
    25.
    发明申请
    Automatic Language Model Update 审中-公开

    公开(公告)号：US20180204565A1

    公开(公告)日：2018-07-19

    申请号：US15922154

    申请日：2018-03-15

    Applicant: Google LLC

    Inventor： Michael H. Cohen ,  Shumeet Baluja ,  Pedro J. Moreno Mengibar

    IPC: G10L15/065 ,  G10L15/26 ,  G10L15/06 ,  G10L15/187

    CPC classification number: G10L15/065 ,  G10L15/06 ,  G10L15/063 ,  G10L15/187 ,  G10L15/26 ,  G10L2015/0635

    Abstract: A method for generating a speech recognition model includes accessing a baseline speech recognition model, obtaining information related to recent language usage from search queries, and modifying the speech recognition model to revise probabilities of a portion of a sound occurrence based on the information. The portion of a sound may include a word. Also, a method for generating a speech recognition model, includes receiving at a search engine from a remote device an audio recording and a transcript that substantially represents at least a portion of the audio recording, synchronizing the transcript with the audio recording, extracting one or more letters from the transcript and extracting the associated pronunciation of the one or more letters from the audio recording, and generating a dictionary entry in a pronunciation dictionary.

    Methods and systems for encoding images
    27.
    发明授权
    Methods and systems for encoding images 有权

    公开(公告)号：US12175740B2

    公开(公告)日：2024-12-24

    申请号：US17614929

    申请日：2019-05-28

    Applicant: Google LLC

    Inventor： Shumeet Baluja ,  Rahul Sukthankar

    IPC: G06V10/94 ,  G06V10/774 ,  G06V10/82

    Abstract: The present disclosure is directed to encoding images. In particular, one or more computing devices can receive data representing one or more machine learning (ML) models configured, at least in part, to encode images comprising objects of a particular type. The computing device(s) can receive data representing an image comprising one or more objects of the particular type. The computing device(s) can generate, based at least in part on the data representing the image and the data representing the ML model(s), data representing an encoded version of the image that alters at least a portion of the image comprising the object(s) such that when the encoded version of the image is decoded, the object(s) are unrecognizable as being of the particular type by one or more object-recognition ML models based at least in part upon which the ML model(s) configured to encode the images were trained.

    Methods, systems, and media for seamless audio melding between songs in a playlist
    28.
    发明授权
    Methods, systems, and media for seamless audio melding between songs in a playlist 有权

    公开(公告)号：US12154593B2

    公开(公告)日：2024-11-26

    申请号：US18204720

    申请日：2023-06-01

    Applicant: Google LLC

    Inventor： Michele Covell ,  Shumeet Baluja

    IPC: G11B27/02 ,  G10L21/10 ,  G11B27/10

    Abstract: In accordance with some embodiments of the disclosed subject matter, mechanisms for seamless audio melding between audio items in a playlist are provided. In some embodiments, a method for transitioning between audio items in playlists is provided, comprising: identifying a sequence of audio items in a playlist of audio items, wherein the sequence of audio items includes a first audio item and a second audio item that is to be played subsequent to the first audio item; and modifying an end portion of the first audio item and a beginning portion of the second audio item, where the end portion of the first audio item and the beginning portion of the second audio item are to be played concurrently to transition between the first audio item and the second audio item, wherein the end portion of the first audio item and the beginning portion of the second audio item have an overlap duration, and wherein modifying the end portion of the first audio item and the beginning portion of the second audio item comprises: generating a first spectrogram corresponding to the end portion of the first audio item and a second spectrogram corresponding to the beginning portion of the second audio item; identifying, for each frequency band in a series of frequency bands, a window over which the first spectrogram within the end portion of the first audio item and the second spectrogram within the beginning portion of the second audio item have a particular cross-correlation; modifying, for each frequency band in the series of frequency bands, the end portion of the first spectrogram and the beginning portion of the second spectrogram such that amplitudes of frequencies within the frequency band decrease within the first spectrogram over the end portion of the first spectrogram and that amplitudes of frequencies within the frequency band increase within the second spectrogram over the beginning portion of the second spectrogram; and generating a modified version of the first audio item the includes the modified end portion of the first audio item based on the modified end portion of the first spectrogram and generating a modified version of the second audio item that includes the modified beginning portion of the second audio item based on the modified beginning portion of the second spectrogram.

    Look-up table based neural networks
    29.
    发明授权
    Look-up table based neural networks 有权

    公开(公告)号：US12118466B2

    公开(公告)日：2024-10-15

    申请号：US17978026

    申请日：2022-10-31

    Applicant: Google LLC

    Inventor： Michele Covell ,  David Marwood ,  Shumeet Baluja ,  Nicholas Johnston

    IPC: G06F1/03 ,  G06N3/048 ,  G06N3/08

    CPC classification number: G06N3/08 ,  G06F1/03 ,  G06N3/048

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing a network input using a neural network to generate a network output for the network input. One of the methods includes maintaining, for each of the plurality of neural network layers, a respective look-up table that maps each possible combination of a quantized input index and a quantized weight index to a multiplication result; and generating a network output from a network input, comprising, for each of the neural network layers: receiving data specifying a quantized input to the neural network layer, the quantized input comprising a plurality of quantized input values; and generating a layer output for the neural network layer from the quantized input to the neural network layer using the respective look-up table for the neural network layer.

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