公开(公告)号：US20250005048A1

公开(公告)日：2025-01-02

申请号：US18345990

申请日：2023-06-30

Applicant: Adobe Inc.

Inventor： Abhinav JAVA ,  Surgan JANDIAL ,  Shripad DESHMUKH ,  Milan AGGARWAL ,  Mausoom SARKAR ,  Balaji KRISHNAMURTHY ,  Arneh JAIN

IPC: G06F16/332

Abstract: Embodiments are disclosed for one-shot document snippet search. A method of one-shot document snippet search may include obtaining a query snippet and a target document. A multi-modal snippet detection model combines first multi-modal features from the query snippet and second multi-modal features from the target document to create a feature volume. The multi-modal snippet detection model identifies one or more matching snippets from the target document based on the feature volume.

公开(公告)号：US20240330682A1

公开(公告)日：2024-10-03

申请号：US18295094

申请日：2023-04-03

Applicant: Adobe Inc.

Inventor： Surgan JANDIAL ,  Siddarth Ramesh ,  Piyush Gupta ,  Gauri Gupta ,  Balaji Krishnamurthy

IPC: G06N3/08 ,  G06N3/0455

CPC classification number: G06N3/08 ,  G06N3/0455

Abstract: Systems and methods for generating synthetic tabular data for machine learning and other applications are provided. In some embodiments, a variational autoencoder is trained to learn inter-feature correlations found in tabular data collected from real data sources. The trained variational autoencoder is used to train a generator model of a Generative Adversarial Network (GAN) to generate synthetic tabular data that exhibits the inter-feature correlation distribution found in the tabular data collected from real data sources. In some embodiments, processing devices perform operations comprising: receiving a set of tabular data records, each record comprising a plurality of features; training a first machine learning model using the tabular data records to learn correlations between the plurality of features; and training a second machine learning model, using the first machine learning model, to generate a synthetic tabular data records based at least on the one or more correlations between the plurality of features.