公开(公告)号：US20230377226A1

公开(公告)日：2023-11-23

申请号：US18199883

申请日：2023-05-19

Applicant: Google LLC

Inventor： Chitwan Saharia ,  William Chan ,  Mohammad Norouzi ,  Saurabh Saxena ,  Yi Li ,  Jay Ha Whang ,  David James Fleet ,  Jonathan Ho

IPC: G06T11/60 ,  G06T3/40 ,  G06T5/00 ,  G06F40/40 ,  G06F40/284 ,  G06N3/08

CPC classification number: G06T11/60 ,  G06T3/4053 ,  G06T5/002 ,  G06F40/40 ,  G06F40/284 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating images. In one aspect, a method includes: receiving an input text prompt including a sequence of text tokens in a natural language; processing the input text prompt using a text encoder neural network to generate a set of contextual embeddings of the input text prompt; and processing the contextual embeddings through a sequence of generative neural networks to generate a final output image that depicts a scene that is described by the input text prompt.

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.

公开(公告)号：US11087504B2

公开(公告)日：2021-08-10

申请号：US16494386

申请日：2018-05-21

Applicant: Google LLC

Inventor： Sergio Guadarrama Cotado ,  Jonathon Shlens ,  David Bieber ,  Mohammad Norouzi ,  Kevin Patrick Murphy ,  Ryan Lienhart Dahl

IPC: G06T11/00 ,  G06T3/40

Abstract: Systems and methods for transforming grayscale images into color images using deep neural networks are described. One of the systems include one or more computers and one or more storage devices storing instructions that, when executed by one or more computers, cause the one or more computers to implement a coloring neural network, a refinement neural network, and a subsystem. The coloring neural network is configured to receive a first grayscale image having a first resolution and to process the first grayscale image to generate a first color image having a second resolution lower than the first resolution. The subsystem processes the first color image to generate a set of intermediate image outputs. The refinement neural network is configured to receive the set intermediate image outputs, and to process the set of intermediate image outputs to generate a second color image having a third resolution higher than the second resolution.

公开(公告)号：US20210158162A1

公开(公告)日：2021-05-27

申请号：US17103827

申请日：2020-11-24

Applicant: Google LLC

Inventor： Danijar Hafner ,  Mohammad Norouzi ,  Timothy Paul Lillicrap

IPC: G06N3/08 ,  G06K9/62 ,  G06F30/27

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection policy neural network used to select an action to be performed by an agent interacting with an environment. In one aspect, a method includes: receiving a latent representation characterizing a current state of the environment; generating a trajectory of latent representations that starts with the received latent representation; for each latent representation in the trajectory: determining a predicted reward; and processing the state latent representation using a value neural network to generate a predicted state value; determining a corresponding target state value for each latent representation in the trajectory; determining, based on the target state values, an update to the current values of the policy neural network parameters; and determining an update to the current values of the value neural network parameters.

公开(公告)号：US10733502B2

公开(公告)日：2020-08-04

申请号：US16504934

申请日：2019-07-08

Applicant: Google LLC

Inventor： Ofir Nachum ,  Mohammad Norouzi ,  Dale Eric Schuurmans ,  Kelvin Xu

IPC: G06N3/04 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a policy neural network used to select actions to be performed by a reinforcement learning agent interacting with an environment. In one aspect, a method includes obtaining path data defining a path through the environment traversed by the agent. A consistency error is determined for the path from a combined reward, first and last soft-max state values, and a path likelihood. A value update for the current values of the policy neural network parameters is determined from at least the consistency error. The value update is used to adjust the current values of the policy neural network parameters.

公开(公告)号：US20200098144A1

公开(公告)日：2020-03-26

申请号：US16494386

申请日：2018-05-21

Applicant: Google LLC

Inventor： Mohammad Norouzi ,  Jonathon Shiens ,  David Bieber ,  Sergio Guadarrama Cotado ,  Kevin Patrick Murphy ,  Ryan Lienhart Dahl

IPC: G06T11/00 ,  G06T7/90 ,  G06T3/40

Abstract: Systems and methods for transforming grayscale images into color images using deep neural networks are described. One of the systems include one or more computers and one or more storage devices storing instructions that, when executed by one or more computers, cause the one or more computers to implement a coloring neural network, a refinement neural network, and a subsystem. The coloring neural network is configured to receive a first grayscale image having a first resolution and to process the first grayscale image to generate a first color image having a second resolution lower than the first resolution. The subsystem processes the first color image to generate a set of intermediate image outputs. The refinement neural network is configured to receive the set intermediate image outputs, and to process the set of intermediate image outputs to generate a second color image having a third resolution higher than the second resolution.

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.

公开(公告)号：US20190332922A1

公开(公告)日：2019-10-31

申请号：US16504934

申请日：2019-07-08

Applicant: Google LLC

Inventor： Ofir Nachum ,  Mohammad Norouzi ,  Dale Eric Schuurmans ,  Kelvin Xu

IPC: G06N3/04 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a policy neural network used to select actions to be performed by a reinforcement learning agent interacting with an environment. In one aspect, a method includes obtaining path data defining a path through the environment traversed by the agent. A consistency error is determined for the path from a combined reward, first and last soft-max state values, and a path likelihood. A value update for the current values of the policy neural network parameters is determined from at least the consistency error. The value update is used to adjust the current values of the policy neural network parameters.

公开(公告)号：US20250086462A1

公开(公告)日：2025-03-13

申请号：US18960623

申请日：2024-11-26

Applicant: Google LLC

Inventor： Ting Chen ,  Simon Komblith ,  Mohammad Norouzi ,  Geoffrey Everest Hinton ,  Kevin Jordan Swersky

IPC: G06N3/084 ,  G06F18/21 ,  G06F18/214 ,  G06F18/241 ,  G06N3/08 ,  G06V10/764 ,  G06V10/774 ,  G06V10/778

Abstract: Systems, methods, and computer program products for performing semi-supervised contrastive learning of visual representations are provided. For example, the present disclosure provides systems and methods that leverage particular data augmentation schemes and a learnable nonlinear transformation between the representation and the contrastive loss to provide improved visual representations. Further, the present disclosure also provides improvements for semi-supervised contrastive learning. For example, computer-implemented method may include performing semi-supervised contrastive learning based on a set of one or more unlabeled training data, generating an image classification model based on a portion of a plurality of layers in a projection head neural network used in performing the contrastive learning, performing fine-tuning of the image classification model based on a set of one or more labeled training data, and after performing the fine-tuning, distilling the image classification model to a student model comprising a relatively smaller number of parameters than the image classification model.

公开(公告)号：US12242818B2

公开(公告)日：2025-03-04

申请号：US17797872

申请日：2021-02-08

Applicant: Google LLC

Inventor： William Chan ,  Chitwan Saharia ,  Geoffrey E. Hinton ,  Mohammad Norouzi ,  Navdeep Jaitly

IPC: G06F40/47 ,  G06F40/284

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for sequence modeling. One of the methods includes receiving an input sequence having a plurality of input positions; determining a plurality of blocks of consecutive input positions; processing the input sequence using a neural network to generate a latent alignment, comprising, at each of a plurality of input time steps: receiving a partial latent alignment from a previous input time step; selecting an input position in each block, wherein the token at the selected input position of the partial latent alignment in each block is a mask token; and processing the partial latent alignment and the input sequence using the neural network to generate a new latent alignment, wherein the new latent alignment comprises, at the selected input position in each block, an output token or a blank token; and generating, using the latent alignment, an output sequence.