公开(公告)号：US11494561B2

公开(公告)日：2022-11-08

申请号：US16984337

申请日：2020-08-04

Applicant: Google LLC

Inventor： Noam M. Shazeer ,  Aidan Nicholas Gomez ,  Lukasz Mieczyslaw Kaiser ,  Jakob D. Uszkoreit ,  Llion Owen Jones ,  Niki J. Parmar ,  Ashish Teku Vaswani

IPC: G06F40/284 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for training a machine learning model to perform multiple machine learning tasks from multiple machine learning domains. One system includes a machine learning model that includes multiple input modality neural networks corresponding to respective different modalities and being configured to map received data inputs of the corresponding modality to mapped data inputs from a unified representation space; an encoder neural network configured to process mapped data inputs from the unified representation space to generate respective encoder data outputs; a decoder neural network configured to process encoder data outputs to generate respective decoder data outputs from the unified representation space; and multiple output modality neural networks corresponding to respective different modalities and being configured to map decoder data outputs to data outputs of the corresponding modality.

公开(公告)号：US20220215654A1

公开(公告)日：2022-07-07

申请号：US17606976

申请日：2020-05-22

Applicant: Google LLC

Inventor： Jonathon Shlens ,  Ashish Teku Vaswani ,  Niki J. Parmar ,  Prajit Ramachandran ,  Anselm Caelifer Levskaya ,  Irwan Bello

IPC: G06V10/82 ,  G06N3/04 ,  G06T9/00

Abstract: A system implemented as computer programs on one or more computers in one or more locations that implements a computer vision model is described. The computer vision model includes a positional local self-attention layer that is configured to receive an input feature map and to generate an output feature map. For each input element in the input feature map, the positional local self-attention layer generates a respective output element for the output feature map by generating a memory block including neighboring input elements around the input element, generates a query vector using the input element and a query weight matrix, for each neighboring element in the memory block, performs positional local self-attention operations to generate a temporary output element, and generates the respective output element by summing temporary output elements of the neighboring elements in the memory block.

公开(公告)号：US11138392B2

公开(公告)日：2021-10-05

申请号：US16521780

申请日：2019-07-25

Applicant: Google LLC

Inventor： Zhifeng Chen ,  Macduff Richard Hughes ,  Yonghui Wu ,  Michael Schuster ,  Xu Chen ,  Llion Owen Jones ,  Niki J. Parmar ,  George Foster ,  Orhan Firat ,  Ankur Bapna ,  Wolfgang Macherey ,  Melvin Jose Johnson Premkumar

IPC: G06F40/58 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for machine translation using neural networks. In some implementations, a text in one language is translated into a second language using a neural network model. The model can include an encoder neural network comprising a plurality of bidirectional recurrent neural network layers. The encoding vectors are processed using a multi-headed attention module configured to generate multiple attention context vectors for each encoding vector. A decoder neural network generates a sequence of decoder output vectors using the attention context vectors. The decoder output vectors can represent distributions over various language elements of the second language, allowing a translation of the text into the second language to be determined based on the sequence of decoder output vectors.

公开(公告)号：US20200034436A1

公开(公告)日：2020-01-30

申请号：US16521780

申请日：2019-07-25

Applicant: Google LLC

Inventor： Zhifeng Chen ,  Macduff Richard Hughes ,  Yonghui Wu ,  Michael Schuster ,  Xu Chen ,  Llion Owen Jones ,  Niki J. Parmar ,  George Foster ,  Orhan Firat ,  Ankur Bapna ,  Wolfgang Macherey ,  Melvin Jose Johnson Premkumar

IPC: G06F17/28 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for machine translation using neural networks. In some implementations, a text in one language is translated into a second language using a neural network model. The model can include an encoder neural network comprising a plurality of bidirectional recurrent neural network layers. The encoding vectors are processed using a multi-headed attention module configured to generate multiple attention context vectors for each encoding vector. A decoder neural network generates a sequence of decoder output vectors using the attention context vectors. The decoder output vectors can represent distributions over various language elements of the second language, allowing a translation of the text into the second language to be determined based on the sequence of decoder output vectors.

公开(公告)号：US20190392319A1

公开(公告)日：2019-12-26

申请号：US16559392

申请日：2019-09-03

Applicant: Google LLC

Inventor： Noam M. Shazeer ,  Aidan Nicholas Gomez ,  Lukasz Mieczyslaw Kaiser ,  Jakob D. Uszkoreit ,  Llion Owen Jones ,  Niki J. Parmar ,  Illia Polosukhin ,  Ashish Teku Vaswani

IPC: G06N3/08 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output sequence from an input sequence. In one aspect, one of the systems includes an encoder neural network configured to receive the input sequence and generate encoded representations of the network inputs, the encoder neural network comprising a sequence of one or more encoder subnetworks, each encoder subnetwork configured to receive a respective encoder subnetwork input for each of the input positions and to generate a respective subnetwork output for each of the input positions, and each encoder subnetwork comprising: an encoder self-attention sub-layer that is configured to receive the subnetwork input for each of the input positions and, for each particular input position in the input order: apply an attention mechanism over the encoder subnetwork inputs using one or more queries derived from the encoder subnetwork input at the particular input position.

公开(公告)号：US12217173B2

公开(公告)日：2025-02-04

申请号：US17467096

申请日：2021-09-03

Applicant: Google LLC

Inventor： Noam M. Shazeer ,  Aidan Nicholas Gomez ,  Lukasz Mieczyslaw Kaiser ,  Jakob D. Uszkoreit ,  Llion Owen Jones ,  Niki J. Parmar ,  Illia Polosukhin ,  Ashish Teku Vaswani

IPC: G06N20/00 ,  G06N3/04 ,  G06N3/045 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output sequence from an input sequence. In one aspect, one of the systems includes an encoder neural network configured to receive the input sequence and generate encoded representations of the network inputs, the encoder neural network comprising a sequence of one or more encoder subnetworks, each encoder subnetwork configured to receive a respective encoder subnetwork input for each of the input positions and to generate a respective subnetwork output for each of the input positions, and each encoder subnetwork comprising: an encoder self-attention sub-layer that is configured to receive the subnetwork input for each of the input positions and, for each particular input position in the input order: apply an attention mechanism over the encoder subnetwork inputs using one or more queries derived from the encoder subnetwork input at the particular input position.

公开(公告)号：US12142034B2

公开(公告)日：2024-11-12

申请号：US18388178

申请日：2023-11-08

Applicant: Google LLC

Inventor： Noam M. Shazeer ,  Lukasz Mieczyslaw Kaiser ,  Jakob D. Uszkoreit ,  Niki J. Parmar ,  Ashish Teku Vaswani

IPC: G06V10/82 ,  G06F18/21 ,  G06F18/213 ,  G06F18/28 ,  G06N3/04 ,  G06N3/084 ,  G06T3/4053 ,  G06V10/56 ,  G06V10/77

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output image. In one aspect, one of the methods includes generating the output image intensity value by intensity value according to a generation order of pixel-color channel pairs from the output image, comprising, for each particular generation order position in the generation order: generating a current output image representation of a current output image, processing the current output image representation using a decoder neural network to generate a probability distribution over possible intensity values for the pixel-color channel pair at the particular generation order position, wherein the decoder neural network includes one or more local masked self-attention sub-layers; and selecting an intensity value for the pixel-color channel pair at the particular generation order position using the probability distribution.

公开(公告)号：US20220083746A1

公开(公告)日：2022-03-17

申请号：US17459041

申请日：2021-08-27

Applicant: Google LLC

Inventor： Zhifeng Chen ,  Macduff Richard Hughes ,  Yonghui Wu ,  Michael Schuster ,  Xu Chen ,  Llion Owen Jones ,  Niki J. Parmar ,  George Foster ,  Orhan Firat ,  Ankur Bapna ,  Wolfgang Macherey ,  Melvin Jose Johnson Premkumar

IPC: G06F40/58 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for machine translation using neural networks. In some implementations, a text in one language is translated into a second language using a neural network model. The model can include an encoder neural network comprising a plurality of bidirectional recurrent neural network layers. The encoding vectors are processed using a multi-headed attention module configured to generate multiple attention context vectors for each encoding vector. A decoder neural network generates a sequence of decoder output vectors using the attention context vectors. The decoder output vectors can represent distributions over various language elements of the second language, allowing a translation of the text into the second language to be determined based on the sequence of decoder output vectors.

公开(公告)号：US20210064924A1

公开(公告)日：2021-03-04

申请号：US17098271

申请日：2020-11-13

Applicant: Google LLC

Inventor： Noam M. Shazeer ,  Lukasz Mieczyslaw Kaiser ,  Jakob D. Uszkoreit ,  Niki J. Parmar ,  Ashish Teku Vaswani

IPC: G06K9/62 ,  G06K9/46 ,  G06N3/08 ,  G06N3/04 ,  G06T3/40

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output image. In one aspect, one of the methods includes generating the output image intensity value by intensity value according to a generation order of pixel—color channel pairs from the output image, comprising, for each particular generation order position in the generation order: generating a current output image representation of a current output image, processing the current output image representation using a decoder neural network to generate a probability distribution over possible intensity values for the pixel—color channel pair at the particular generation order position, wherein the decoder neural network includes one or more local masked self-attention sub-layers; and selecting an intensity value for the pixel—color channel pair at the particular generation order position using the probability distribution.

公开(公告)号：US20180341860A1

公开(公告)日：2018-11-29

申请号：US16021971

申请日：2018-06-28

Applicant: Google LLC

Inventor： Noam M. Shazeer ,  Aidan Nicholas Gomez ,  Lukasz Mieczyslaw Kaiser ,  Jakob D. Uszkoreit ,  Llion Owen Jones ,  Niki J. Parmar ,  Illia Polosukhin ,  Ashish Teku Vaswani

IPC: G06N3/08 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output sequence from an input sequence. In one aspect, one of the systems includes an encoder neural network configured to receive the input sequence and generate encoded representations of the network inputs, the encoder neural network comprising a sequence of one or more encoder subnetworks, each encoder subnetwork configured to receive a respective encoder subnetwork input for each of the input positions and to generate a respective subnetwork output for each of the input positions, and each encoder subnetwork comprising: an encoder self-attention sub-layer that is configured to receive the subnetwork input for each of the input positions and, for each particular input position in the input order: apply an attention mechanism over the encoder subnetwork inputs using one or more queries derived from the encoder subnetwork input at the particular input position.