公开(公告)号：US11995048B2

公开(公告)日：2024-05-28

申请号：US17036453

申请日：2020-09-29

Applicant: ADOBE INC.

Inventor： Handong Zhao ,  Yikun Xian ,  Sungchul Kim ,  Tak Yeon Lee ,  Nikhil Belsare ,  Shashi Kant Rai ,  Vasanthi Holtcamp ,  Thomas Jacobs ,  Duy-Trung T Dinh ,  Caroline Jiwon Kim

IPC: G06F16/00 ,  G06F16/21 ,  G06F18/2115 ,  G06F18/214 ,  G06F18/2431 ,  G06N3/08 ,  G06V30/262

CPC classification number: G06F16/213 ,  G06F18/2115 ,  G06F18/2148 ,  G06F18/2431 ,  G06N3/08 ,  G06V30/274

Abstract: Systems and methods for lifelong schema matching are described. The systems and methods include receiving data comprising a plurality of information categories, classifying each information category according to a schema comprising a plurality of classes, wherein the classification is performed by a neural network classifier trained based on a lifelong learning technique using a plurality of exemplar training sets, wherein each of the exemplar training sets includes a plurality of examples corresponding to one of the classes, and wherein the examples are selected based on a metric indicating how well each of the examples represents the corresponding class, and adding the data to a database based on the classification, wherein the database is organized according to the schema.

公开(公告)号：US11544281B2

公开(公告)日：2023-01-03

申请号：US17100618

申请日：2020-11-20

Applicant: Adobe Inc.

Inventor： Subrata Mitra ,  Nikhil Sheoran ,  Anup Rao ,  Tung Mai ,  Sapthotharan Krishnan Nair ,  Shivakumar Vaithyanathan ,  Thomas Jacobs ,  Ghetia Siddharth ,  Jatin Varshney ,  Vikas Maddukuri ,  Laxmikant Mishra

IPC: G06F16/2458 ,  G06F16/215 ,  G06F16/28 ,  G06F16/22 ,  G06N20/00 ,  G06K9/62

Abstract: In some embodiments, a model training system trains a sample generation model configured to generate synthetic data entries for a dataset. The sample generation model includes a prior model for generating an estimated latent vector from a partially observed data entry, a proposal model for generating a latent vector from a data entry of the dataset and a mask corresponding to the partially observed data entry, and a generative model for generating the synthetic data entries from the latent vector and the partially observed data entry. The model training system trains the sample generation model to optimize an objective function that includes a first term determined using the synthetic data entries and a second term determined using the estimated latent vector and the latent vector. The trained sample generation model can be executed on a client computing device to service queries using the generated synthetic data entries.

公开(公告)号：US20220164346A1

公开(公告)日：2022-05-26

申请号：US17100618

申请日：2020-11-20

Applicant: Adobe Inc.

Inventor： Subrata Mitra ,  Nikhil Sheoran ,  Anup Rao ,  Tung Mai ,  Sapthotharan Krishnan Nair ,  Shivakumar Vaithyanathan ,  Thomas Jacobs ,  Ghetia Siddharth ,  Jatin Varshney ,  Vikas Maddukuri ,  Laxmikant Mishra

IPC: G06F16/2458 ,  G06F16/215 ,  G06F16/28 ,  G06F16/22 ,  G06K9/62 ,  G06N20/00

Abstract: In some embodiments, a model training system trains a sample generation model configured to generate synthetic data entries for a dataset. The sample generation model includes a prior model for generating an estimated latent vector from a partially observed data entry, a proposal model for generating a latent vector from a data entry of the dataset and a mask corresponding to the partially observed data entry, and a generative model for generating the synthetic data entries from the latent vector and the partially observed data entry. The model training system trains the sample generation model to optimize an objective function that includes a first term determined using the synthetic data entries and a second term determined using the estimated latent vector and the latent vector. The trained sample generation model can be executed on a client computing device to service queries using the generated synthetic data entries.

公开(公告)号：US20220100714A1

公开(公告)日：2022-03-31

申请号：US17036453

申请日：2020-09-29

Applicant: ADOBE INC.

Inventor： Handong Zhao ,  Yikun Xian ,  Sungchul Kim ,  Tak Yeon Lee ,  Nikhil Belsare ,  Shashi Kant Rai ,  Vasanthi Holtcamp ,  Thomas Jacobs ,  Duy-Trung T. Dinh ,  Caroline Jiwon Kim

IPC: G06F16/21 ,  G06K9/62 ,  G06N3/08 ,  G06K9/72

Abstract: Systems and methods for lifelong schema matching are described. The systems and methods include receiving data comprising a plurality of information categories, classifying each information category according to a schema comprising a plurality of classes, wherein the classification is performed by a neural network classifier trained based on a lifelong learning technique using a plurality of exemplar training sets, wherein each of the exemplar training sets includes a plurality of examples corresponding to one of the classes, and wherein the examples are selected based on a metric indicating how well each of the examples represents the corresponding class, and adding the data to a database based on the classification, wherein the database is organized according to the schema.