公开(公告)号：US20220075944A1

公开(公告)日：2022-03-10

申请号：US17432259

申请日：2020-02-19

Applicant: Google LLC

Inventor： Nan Du ,  Linh Trans ,  Yu-hui Chen ,  Izhak Shafran

IPC: G06F40/284 ,  G06F40/295 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for extracting entities from conversation transcript data. One of the methods includes obtaining a conversation transcript sequence, processing the conversation transcript sequence using a span detection neural network configured to generate a set of text token spans; and for each text token span: processing a span representation using an entity name neural network to generate an entity name probability distribution over a set of entity names, each probability in the entity name probability distribution representing a likelihood that a corresponding entity name is a name of the entity referenced by the text token span; and processing the span representation using an entity status neural network to generate an entity status probability distribution over a set of entity statuses.

公开(公告)号：US20210065066A1

公开(公告)日：2021-03-04

申请号：US17008338

申请日：2020-08-31

Applicant: Google LLC

Inventor： Yuan Xue ,  Dengyong Zhou ,  Nan Du ,  Andrew Mingbo Dai ,  Zhen Xu ,  Kun Zhang ,  Yingwei Cui

IPC: G06N20/20 ,  G06N3/02 ,  G06F17/18 ,  G06F17/16

Abstract: A deep state space generative model is augmented with intervention prediction. The state space model provides a principled way to capture the interactions among observations, interventions, critical event occurrences, true states, and associated uncertainty. The state space model can include a discrete-time hazard rate model that provides flexible fitting of general survival time distributions. The state space model can output a joint prediction of event risk, observation and intervention trajectories based on patterns in temporal progressions, and correlations between past measurements and interventions.

公开(公告)号：US20240378427A1

公开(公告)日：2024-11-14

申请号：US18661499

申请日：2024-05-10

Applicant: Google LLC

Inventor： Slav Petrov ,  Yonghui Wu ,  Andrew M. Dai ,  David Richard So ,  Dmitry Lepikhin ,  Erica Ann Moreira ,  Gaurav Mishra ,  Jonathan Hudson Clark ,  Maxim Krikun ,  Melvin Jose Johnson Premkumar ,  Nan Du ,  Orhan Firat ,  Rohan Anil ,  Siamak Shakeri ,  Xavier Garcia ,  Yanping Huang ,  Yong Cheng ,  Yuanzhong Xu ,  Yujing Zhang ,  Zachary Alexander Nado ,  Eric Jun Jie Ni ,  Kefan Xiao ,  Vladimir Feinberg ,  Jin Young Sohn ,  Aurko Roy

IPC: G06N3/0475 ,  G06F40/284

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network to perform any one or more of a variety of machine learning tasks. For example, the neural network can be configured as a generative neural network, e.g., an autoregressive generative neural network.

公开(公告)号：US20240112027A1

公开(公告)日：2024-04-04

申请号：US18477546

申请日：2023-09-28

Applicant: Google LLC

Inventor： Yanqi Zhou ,  Yanping Huang ,  Yifeng Lu ,  Andrew M. Dai ,  Siamak Shakeri ,  Zhifeng Chen ,  James Laudon ,  Quoc V. Le ,  Da Huang ,  Nan Du ,  David Richard So ,  Daiyi Peng ,  Yingwei Cui ,  Jeffrey Adgate Dean ,  Chang Lan

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing neural architecture search for machine learning models. In one aspect, a method comprises receiving training data for a machine learning, generating a plurality of candidate neural networks for performing the machine learning task, wherein each candidate neural network comprises a plurality of instances of a layer block composed of a plurality of layers, for each candidate neural network, selecting a respective type for each of the plurality of layers from a set of layer types that comprises, training the candidate neural network and evaluating performance scores for the trained candidate neural networks as applied to the machine learning task, and determining a final neural network for performing the machine learning task based at least on the performance scores for the candidate neural networks.

公开(公告)号：US12216999B2

公开(公告)日：2025-02-04

申请号：US17432259

申请日：2020-02-19

Applicant: Google LLC

Inventor： Nan Du ,  Linh Mai Tran ,  Yu-Hui Chen ,  Izhak Shafran

IPC: G06F40/279 ,  G06F40/284 ,  G06F40/295 ,  G06N3/045

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for extracting entities from conversation transcript data. One of the methods includes obtaining a conversation transcript sequence, processing the conversation transcript sequence using a span detection neural network configured to generate a set of text token spans; and for each text token span: processing a span representation using an entity name neural network to generate an entity name probability distribution over a set of entity names, each probability in the entity name probability distribution representing a likelihood that a corresponding entity name is a name of the entity referenced by the text token span; and processing the span representation using an entity status neural network to generate an entity status probability distribution over a set of entity statuses.

公开(公告)号：US20240303464A1

公开(公告)日：2024-09-12

申请号：US18598876

申请日：2024-03-07

Applicant: Google LLC

Inventor： Nan Du ,  Tao Wang ,  Yanqi Zhou ,  Tao Lei ,  Yuanzhong Xu ,  Andrew Mingbo Dai ,  Zhifeng Chen ,  Dewen Zeng ,  Yingwei Cui

IPC: G06N3/04 ,  G06N3/084

CPC classification number: G06N3/04 ,  G06N3/084

Abstract: A method includes providing a first set of data objects to a first skip router of a neural network (NN). The NN includes a first NN layer and a second NN layer. The first set of data objects is subdivided into a first set of skip objects and a first set of non-skip objects based on a first skip logic implemented by the first skip router and a first context of each data object in the first set of data objects. A first set of processed objects is generated based on the first set of non-skip objects and a first layer logic implemented by the first NN layer. Predictions are generated based on a second set of data objects and a second layer logic implemented by the second NN layer. The second set of data objects includes the first set of processed objects and the first set of skip objects.

公开(公告)号：US20250131251A1

公开(公告)日：2025-04-24

申请号：US18834070

申请日：2023-01-30

Applicant: Google LLC

Inventor： Hanxiao Liu ,  Quoc V. Le ,  Yanqi Zhou ,  Tao Lei ,  Yuzhe Zhao ,  Yanping Huang ,  Nan Du ,  Zhifeng Chen ,  Andrew M. Dai ,  James Laudon

IPC: G06N3/048

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a machine learning task on a network input to generate a network output. In one aspect, one of the systems includes a neural network configured to perform the machine learning task, the neural network including one or more expert neural network blocks that each include router that performs expert-choice routing between multiple expert neural networks.

公开(公告)号：US12217144B2

公开(公告)日：2025-02-04

申请号：US17008338

申请日：2020-08-31

Applicant: Google LLC

Inventor： Yuan Xue ,  Dengyong Zhou ,  Nan Du ,  Andrew Mingbo Dai ,  Zhen Xu ,  Kun Zhang ,  Yingwei Cui

IPC: G06N20/20 ,  G06F17/16 ,  G06F17/18 ,  G06N3/02

Abstract: A deep state space generative model is augmented with intervention prediction. The state space model provides a principled way to capture the interactions among observations, interventions, critical event occurrences, true states, and associated uncertainty. The state space model can include a discrete-time hazard rate model that provides flexible fitting of general survival time distributions. The state space model can output a joint prediction of event risk, observation and intervention trajectories based on patterns in temporal progressions, and correlations between past measurements and interventions.

公开(公告)号：US20240378441A1

公开(公告)日：2024-11-14

申请号：US18661447

申请日：2024-05-10

Applicant: Google LLC

Inventor： Slav Petrov ,  Yonghui Wu ,  Andrew M. Dai ,  David Richard So ,  Dmitry Lepikhin ,  Erica Ann Moreira ,  Gaurav Mishra ,  Jonathan Hudson Clark ,  Maxim Krikun ,  Melvin Jose Johnson Premkumar ,  Nan Du ,  Orhan Firat ,  Rohan Anil ,  Siamak Shakeri ,  Xavier Garcia ,  Yanping Huang ,  Yong Cheng ,  Yuanzhong Xu ,  Yujing Zhang ,  Zachary Alexander Nado ,  Eric Jun Jie Ni ,  Kefan Xiao ,  Vladimir Feinberg ,  Jin Young Sohn ,  Aurko Roy

IPC: G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network to perform any one or more of a variety of machine learning tasks. For example, the neural network can be configured as a generative neural network, e.g., an autoregressive generative neural network.