公开(公告)号：US20230123941A1

公开(公告)日：2023-04-20

申请号：US18081376

申请日：2022-12-14

Applicant: Google LLC

Inventor： Xiang Wu ,  David Simcha ,  Daniel Holtmann-Rice ,  Sanjiv Kumar ,  Ananda Theertha Suresh ,  Ruiqi Guo ,  Xinnan Yu

IPC: G06F16/33 ,  G06F16/31 ,  G06N20/00

Abstract: The present disclosure provides systems and methods that include or otherwise leverage use of a multiscale quantization model that is configured to provide a quantized dataset. In particular, the multiscale quantization model can receive and perform vector quantization of a first dataset. The multiscale quantization model can generate a residual dataset based at least in part on a result of the vector quantization. The multiscale quantization model can apply a rotation matrix to the residual dataset to generate a rotated residual dataset that includes a plurality of rotated residuals. The multiscale quantization model can perform reparameterization of each rotated residual in the rotated residual dataset into a direction component and a scale component. The multiscale quantization model can perform product quantization of the direction components of the plurality of rotated residuals, and perform scalar quantization of the scale components of the plurality of rotated residuals.

公开(公告)号：US20210064634A1

公开(公告)日：2021-03-04

申请号：US17001850

申请日：2020-08-25

Applicant: Google LLC

Inventor： Ruiqi Guo ,  David Simcha ,  Quan Geng ,  Felix Chern ,  Sanjiv Kumar ,  Xiang Wu

IPC: G06F16/25 ,  G06F16/2457 ,  H03M7/30

Abstract: Generally, the present disclosure is directed to systems and methods of quantizing a database with respect to a novel loss or quantization error function which applies a weight to an error measurement of quantized elements respectively corresponding to the datapoints in the database. The weight is determined based on the magnitude of an inner product between the respective datapoints and a query compared therewith. In contrast to previous work, embodiments of the proposed loss function are responsive to the expected magnitude of an inner product between the respective datapoints and a query compared therewith and can prioritize error reduction for higher-ranked pairings of the query and the datapoints. Thus, the systems and methods of the present disclosure provide solutions to some of the problems with traditional quantization approaches, which regard all error as equally impactful.

公开(公告)号：US20200257668A1

公开(公告)日：2020-08-13

申请号：US16715620

申请日：2019-12-16

Applicant: GOOGLE LLC

Inventor： Xiang Wu ,  David Morris Simcha ,  Sanjiv Kumar ,  Ruiqi Guo

IPC: G06F16/22 ,  G06F16/27 ,  G06F16/2458 ,  G06F16/28 ,  G06F17/16 ,  G06F16/248

Abstract: Techniques of indexing a database and processing a query involve decomposing the residual term according to a projection matrix that is based on a given direction v. For example, for each database element of a partition, the residual for that database element is split into a component parallel to a given direction and a component perpendicular to that direction. The parallel component lies in a one-dimensional subspace spanned by the direction and may be efficiently quantized with a scalar quantization. The perpendicular component is quantized using multiscale quantization techniques. The quantized residual components and the center elements of each partition define the indexed database. Upon receipt of a query from a user, the inner products of q with the residual may be computed efficiently using the quantized residual components. From these inner products, the database elements that are most similar to the query are selected and returned to the user.

公开(公告)号：US11775589B2

公开(公告)日：2023-10-03

申请号：US17001850

申请日：2020-08-25

Applicant: Google LLC

Inventor： Ruiqi Guo ,  David Simcha ,  Quan Geng ,  Felix Chern ,  Sanjiv Kumar ,  Xiang Wu

IPC: G06F16/20 ,  G06F16/906 ,  G06F16/25 ,  H03M7/30 ,  G06F16/2457

CPC classification number: G06F16/906 ,  G06F16/24578 ,  G06F16/258 ,  H03M7/30

Abstract: Generally, the present disclosure is directed to systems and methods of quantizing a database with respect to a novel loss or quantization error function which applies a weight to an error measurement of quantized elements respectively corresponding to the datapoints in the database. The weight is determined based on the magnitude of an inner product between the respective datapoints and a query compared therewith. In contrast to previous work, embodiments of the proposed loss function are responsive to the expected magnitude of an inner product between the respective datapoints and a query compared therewith and can prioritize error reduction for higher-ranked pairings of the query and the datapoints. Thus, the systems and methods of the present disclosure provide solutions to some of the problems with traditional quantization approaches, which regard all error as equally impactful.

公开(公告)号：US11354287B2

公开(公告)日：2022-06-07

申请号：US16715620

申请日：2019-12-16

Applicant: GOOGLE LLC

Inventor： Xiang Wu ,  David Morris Simcha ,  Sanjiv Kumar ,  Ruiqi Guo

IPC: G06F16/20 ,  G06F16/22 ,  G06F16/27 ,  G06F16/248 ,  G06F16/28 ,  G06F17/16 ,  G06F16/2458

Abstract: Techniques of indexing a database and processing a query involve decomposing the residual term according to a projection matrix that is based on a given direction v. For example, for each database element of a partition, the residual for that database element is split into a component parallel to a given direction and a component perpendicular to that direction. The parallel component lies in a one-dimensional subspace spanned by the direction and may be efficiently quantized with a scalar quantization. The perpendicular component is quantized using multiscale quantization techniques. The quantized residual components and the center elements of each partition define the indexed database. Upon receipt of a query from a user, the inner products of q with the residual may be computed efficiently using the quantized residual components. From these inner products, the database elements that are most similar to the query are selected and returned to the user.

公开(公告)号：US20240061889A1

公开(公告)日：2024-02-22

申请号：US18456688

申请日：2023-08-28

Applicant: Google LLC

Inventor： Ruiqi Guo ,  David Simcha ,  Quan Geng ,  Felix Chern ,  Sanjiv Kumar ,  Xiang Wu

IPC: G06F16/906 ,  G06F16/2457 ,  G06F16/25 ,  H03M7/30

CPC classification number: G06F16/906 ,  G06F16/24578 ,  G06F16/258 ,  H03M7/30

Abstract: Generally, the present disclosure is directed to systems and methods of quantizing a database with respect to a novel loss or quantization error function which applies a weight to an error measurement of quantized elements respectively corresponding to the datapoints in the database. The weight is determined based on the magnitude of an inner product between the respective datapoints and a query compared therewith. In contrast to previous work, embodiments of the proposed loss function are responsive to the expected magnitude of an inner product between the respective datapoints and a query compared therewith and can prioritize error reduction for higher-ranked pairings of the query and the datapoints. Thus, the systems and methods of the present disclosure provide solutions to some of the problems with traditional quantization approaches, which regard all error as equally impactful.

公开(公告)号：US11874866B2

公开(公告)日：2024-01-16

申请号：US18081376

申请日：2022-12-14

Applicant: Google LLC

Inventor： Xiang Wu ,  David Simcha ,  Daniel Holtmann-Rice ,  Sanjiv Kumar ,  Ananda Theertha Suresh ,  Ruiqi Guo ,  Xinnan Yu

IPC: G06F16/33 ,  G06F16/31 ,  G06N20/00

CPC classification number: G06F16/3347 ,  G06F16/313 ,  G06F16/319 ,  G06N20/00

Abstract: The present disclosure provides systems and methods that include or otherwise leverage use of a multiscale quantization model that is configured to provide a quantized dataset. In particular, the multiscale quantization model can receive and perform vector quantization of a first dataset. The multiscale quantization model can generate a residual dataset based at least in part on a result of the vector quantization. The multiscale quantization model can apply a rotation matrix to the residual dataset to generate a rotated residual dataset that includes a plurality of rotated residuals. The multiscale quantization model can perform reparameterization of each rotated residual in the rotated residual dataset into a direction component and a scale component. The multiscale quantization model can perform product quantization of the direction components of the plurality of rotated residuals, and perform scalar quantization of the scale components of the plurality of rotated residuals.

公开(公告)号：US11531695B2

公开(公告)日：2022-12-20

申请号：US16638802

申请日：2018-05-14

Applicant: Google LLC

Inventor： Xiang Wu ,  David Simcha ,  Daniel Holtmann-Rice ,  Sanjiv Kumar ,  Ananda Theertha Suresh ,  Ruiqi Guo ,  Xinnan Yu

IPC: G06F16/33 ,  G06F16/31 ,  G06N20/00

Abstract: The present disclosure provides systems and methods that include or otherwise leverage use of a multiscale quantization model that is configured to provide a quantized dataset. In particular, the multiscale quantization model can receive and perform vector quantization of a first dataset. The multiscale quantization model can generate a residual dataset based at least in part on a result of the vector quantization. The multiscale quantization model can apply a rotation matrix to the residual dataset to generate a rotated residual dataset that includes a plurality of rotated residuals. The multiscale quantization model can perform reparameterization of each rotated residual in the rotated residual dataset into a direction component and a scale component. The multiscale quantization model can perform product quantization of the direction components of the plurality of rotated residuals, and perform scalar quantization of the scale components of the plurality of rotated residuals.

公开(公告)号：US20200183964A1

公开(公告)日：2020-06-11

申请号：US16638802

申请日：2018-05-14

Applicant: Google LLC

Inventor： Xiang Wu ,  David Simcha ,  Daniel Holtmann-Rice ,  Sanjiv Kumar ,  Ananda Theertha Suresh ,  Ruiqi Guo ,  Xinnan Yu

IPC: G06F16/33 ,  G06N20/00 ,  G06F16/31

Abstract: The present disclosure provides systems and methods that include or otherwise leverage use of a multiscale quantization model that is configured to provide a quantized dataset. In particular, the multiscale quantization model can receive and perform vector quantization of a first dataset. The multiscale quantization model can generate a residual dataset based at least in part on a result of the vector quantization. The multiscale quantization model can apply a rotation matrix to the residual dataset to generate a rotated residual dataset that includes a plurality of rotated residuals. The multiscale quantization model can perform reparameterization of each rotated residual in the rotated residual dataset into a direction component and a scale component. The multiscale quantization model can perform product quantization of the direction components of the plurality of rotated residuals, and perform scalar quantization of the scale components of the plurality of rotated residuals.