公开(公告)号：US20240114158A1

公开(公告)日：2024-04-04

申请号：US18529173

申请日：2023-12-05

Applicant: Google LLC

Inventor： Vihan Jain ,  Joonseok Lee ,  Ming Zhao ,  Sheide Chammas ,  Hexiang Hu ,  Bowen Zhang ,  Fei Sha ,  Tze Way Eugene Ie

IPC: H04N19/30 ,  G06N20/00 ,  H04N19/172

CPC classification number: H04N19/30 ,  G06N20/00 ,  H04N19/172

Abstract: A computer-implemented method for generating video representations utilizing a hierarchical video encoder includes obtaining a video, wherein the video includes a plurality of frames, processing each of the plurality of frames with a machine-learned frame-level encoder model to respectively generate a plurality of frame representations for the plurality of frames, the plurality of frame representations respective to the plurality of frames determining a plurality of segment representations representative of a plurality of video segments including one or more of the plurality of frames, the plurality of segment representations based at least in part on the plurality of frame representations, processing the plurality of segment representations with a machine-learned segment-level encoder model to generate a plurality of contextualized segment representations, determining a video representation based at least in part on the plurality of contextualized segment representations, and providing the video representation as an output.

公开(公告)号：US11475355B2

公开(公告)日：2022-10-18

申请号：US16288279

申请日：2019-02-28

Applicant: Google LLC

Inventor： Tze Way Eugene Ie ,  Sanmit Santosh Narvekar ,  Craig Edgar Boutilier

IPC: G06N20/00 ,  G06N5/04

Abstract: A computing system for simulating allocation of resources to a plurality of entities is disclosed. The computing system can be configured to input an entity profile that describes a preference and/or demand of a simulated entity into a reinforcement learning agent model and receive, as an output of the reinforcement learning agent model, an allocation output that describes a resource allocation for the simulated entity. The computing system can select one or more resources based on the resource allocation described by the allocation output and provide the resource(s) to an entity model that is configured to simulate a simulated response output that describes a response of the simulated entity. The computing system can receive, as an output of the entity model, the simulated response output and update a resource profile that describes the at least one resource and/or the entity profile based on the simulated response output.

公开(公告)号：US10438129B1

公开(公告)日：2019-10-08

申请号：US14586043

申请日：2014-12-30

Applicant: Google LLC

Inventor： Yoram Singer ,  Tal Shaked ,  Tushar Deepak Chandra ,  Tze Way Eugene Ie

IPC: G06N20/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training machine learning systems. One of the methods includes receiving a plurality of training examples; and training a machine learning system on each of the plurality of training examples to determine trained values for weights of a machine learning model, wherein training the machine learning system comprises: assigning an initial value for a regularization penalty for a particular weight for a particular feature; and adjusting the initial value for the regularization penalty for the particular weight for the particular feature during the training of the machine learning system.

公开(公告)号：US20200250575A1

公开(公告)日：2020-08-06

申请号：US16288279

申请日：2019-02-28

Applicant: Google LLC

Inventor： Tze Way Eugene Ie ,  Sanmit Santosh Narvekar ,  Craig Edgar Boutilier

IPC: G06N20/00 ,  G06N5/04

Abstract: A computing system for simulating allocation of resources to a plurality of entities is disclosed. The computing system can be configured to input an entity profile that describes a preference and/or demand of a simulated entity into a reinforcement learning agent model and receive, as an output of the reinforcement learning agent model, an allocation output that describes a resource allocation for the simulated entity. The computing system can select one or more resources based on the resource allocation described by the allocation output and provide the resource(s) to an entity model that is configured to simulate a simulated response output that describes a response of the simulated entity. The computing system can receive, as an output of the entity model, the simulated response output and update a resource profile that describes the at least one resource and/or the entity profile based on the simulated response output.

公开(公告)号：US11663520B1

公开(公告)日：2023-05-30

申请号：US16551610

申请日：2019-08-26

Applicant: Google LLC

Inventor： Yoram Singer ,  Tal Shaked ,  Tushar Deepak Chandra ,  Tze Way Eugene Ie

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training machine learning systems. One of the methods includes receiving a plurality of training examples; and training a machine learning system on each of the plurality of training examples to determine trained values for weights of a machine learning model, wherein training the machine learning system comprises: assigning an initial value for a regularization penalty for a particular weight for a particular feature; and adjusting the initial value for the regularization penalty for the particular weight for the particular feature during the training of the machine learning system.

公开(公告)号：US20230117499A1

公开(公告)日：2023-04-20

申请号：US17967595

申请日：2022-10-17

Applicant: Google LLC

Inventor： Tze Way Eugene Ie ,  Sanmit Santosh Narvekar ,  Craig Edgar Boutilier

IPC: G06N20/00 ,  G06N5/043

Abstract: A computing system for simulating allocation of resources to a plurality of entities is disclosed. The computing system can be configured to input an entity profile that describes a preference and/or demand of a simulated entity into a reinforcement learning agent model and receive, as an output of the reinforcement learning agent model, an allocation output that describes a resource allocation for the simulated entity. The computing system can select one or more resources based on the resource allocation described by the allocation output and provide the resource(s) to an entity model that is configured to simulate a simulated response output that describes a response of the simulated entity. The computing system can receive, as an output of the entity model, the simulated response output and update a resource profile that describes the at least one resource and/or the entity profile based on the simulated response output.

公开(公告)号：US20230103148A1

公开(公告)日：2023-03-30

申请号：US18070556

申请日：2022-11-29

Applicant: Google LLC

Inventor： Vihan Jain ,  Joonseok Lee ,  Ming Zhao ,  Sheide Chammas ,  Hexiang Hu ,  Bowen Zhang ,  Fei Sha ,  Tze Way Eugene Ie

IPC: H04N19/30 ,  G06N20/00 ,  H04N19/172

Abstract: A computer-implemented method for generating video representations utilizing a hierarchical video encoder includes obtaining a video, wherein the video includes a plurality of frames, processing each of the plurality of frames with a machine-learned frame-level encoder model to respectively generate a plurality of frame representations for the plurality of frames, the plurality of frame representations respective to the plurality of frames determining a plurality of segment representations representative of a plurality of video segments including one or more of the plurality of frames, the plurality of segment representations based at least in part on the plurality of frame representations, processing the plurality of segment representations with a machine-learned segment-level encoder model to generate a plurality of contextualized segment representations, determining a video representation based at least in part on the plurality of contextualized segment representations, and providing the video representation as an output.

公开(公告)号：US20220256175A1

公开(公告)日：2022-08-11

申请号：US17162150

申请日：2021-01-29

Applicant: Google LLC

Inventor： Vihan Jain ,  Joonseok Lee ,  Ming Zhao ,  Sheide Chammas ,  Hexiang Hu ,  Bowen Zhang ,  Fei Sha ,  Tze Way Eugene Ie

IPC: H04N19/30 ,  H04N19/172 ,  G06N20/00

Abstract: A computer-implemented method for generating video representations utilizing a hierarchical video encoder includes obtaining a video, wherein the video includes a plurality of frames, processing each of the plurality of frames with a machine-learned frame-level encoder model to respectively generate a plurality of frame representations for the plurality of frames, the plurality of frame representations respective to the plurality of frames determining a plurality of segment representations representative of a plurality of video segments including one or more of the plurality of frames, the plurality of segment representations based at least in part on the plurality of frame representations, processing the plurality of segment representations with a machine-learned segment-level encoder model to generate a plurality of contextualized segment representations, determining a video representation based at least in part on the plurality of contextualized segment representations, and providing the video representation as an output.

公开(公告)号：US12301847B2

公开(公告)日：2025-05-13

申请号：US18529173

申请日：2023-12-05

Applicant: Google LLC

Inventor： Vihan Jain ,  Joonseok Lee ,  Ming Zhao ,  Sheide Chammas ,  Hexiang Hu ,  Bowen Zhang ,  Fei Sha ,  Tze Way Eugene Ie

IPC: H04N19/30 ,  G06N20/00 ,  H04N19/172 ,  H04N19/20 ,  H04N19/40

Abstract: A computer-implemented method for generating video representations utilizing a hierarchical video encoder includes obtaining a video, wherein the video includes a plurality of frames, processing each of the plurality of frames with a machine-learned frame-level encoder model to respectively generate a plurality of frame representations for the plurality of frames, the plurality of frame representations respective to the plurality of frames determining a plurality of segment representations representative of a plurality of video segments including one or more of the plurality of frames, the plurality of segment representations based at least in part on the plurality of frame representations, processing the plurality of segment representations with a machine-learned segment-level encoder model to generate a plurality of contextualized segment representations, determining a video representation based at least in part on the plurality of contextualized segment representations, and providing the video representation as an output.

公开(公告)号：US11876986B2

公开(公告)日：2024-01-16

申请号：US18070556

申请日：2022-11-29

Applicant: Google LLC

Inventor： Vihan Jain ,  Joonseok Lee ,  Ming Zhao ,  Sheide Chammas ,  Hexiang Hu ,  Bowen Zhang ,  Fei Sha ,  Tze Way Eugene Ie

IPC: H04N19/30 ,  H04N19/00 ,  H04N19/172 ,  G06N20/00

CPC classification number: H04N19/30 ,  G06N20/00 ,  H04N19/172

Abstract: A computer-implemented method for generating video representations utilizing a hierarchical video encoder includes obtaining a video, wherein the video includes a plurality of frames, processing each of the plurality of frames with a machine-learned frame-level encoder model to respectively generate a plurality of frame representations for the plurality of frames, the plurality of frame representations respective to the plurality of frames determining a plurality of segment representations representative of a plurality of video segments including one or more of the plurality of frames, the plurality of segment representations based at least in part on the plurality of frame representations, processing the plurality of segment representations with a machine-learned segment-level encoder model to generate a plurality of contextualized segment representations, determining a video representation based at least in part on the plurality of contextualized segment representations, and providing the video representation as an output.