公开(公告)号：US20210233080A1

公开(公告)日：2021-07-29

申请号：US16751880

申请日：2020-01-24

Applicant: Adobe Inc.

Inventor： Shubhranshu Shekhar ,  Deepak Pai ,  Sriram Ravindran

IPC: G06Q20/40 ,  G06N3/08 ,  G06Q30/06

Abstract: The present disclosure relates to utilizing a graph convolutional neural network to generate similarity probabilities between pairs of digital identities associated with digital transactions based on time dependencies for use in identifying fraudulent transactions. For example, the disclosed systems can generate a transaction graph that includes nodes corresponding to digital identities. The disclosed systems can utilize a time-dependent graph convolutional neural network to generate node embeddings for the nodes based on the edge connections of the transaction graph. Further, the disclosed systems can utilize the node embeddings to determine whether a digital identity is associated with a fraudulent transaction.

公开(公告)号：US20230393960A1

公开(公告)日：2023-12-07

申请号：US17805377

申请日：2022-06-03

Applicant: Adobe Inc.

Inventor： Meghanath Macha Yadagiri ,  Anish Narang ,  Deepak Pai ,  Sriram Ravindran ,  Vijay Srivastava

IPC: G06F11/34 ,  G06K9/62 ,  G06N20/00

CPC classification number: G06F11/3452 ,  G06K9/6267 ,  G06K9/6263 ,  G06N20/00

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that control bias in machine learning models by utilizing a fairness deviation constraint to learn a decision matrix that modifies machine learning model predictions. In one or more embodiments, the disclosed systems generate, utilizing a machine learning model, predicted classification probabilities from a plurality of samples comprising a plurality of values for a data attribute. Moreover, the disclosed systems determine utilizing a decision matrix and the predicted classification probabilities, that the machine learning model fails to satisfy a fairness deviation constraint with respect to a value of the data attribute. In addition, the disclosed systems generate a modified decision matrix for the machine learning model to satisfy the fairness deviation constraint by selecting a modified decision threshold for the value of the data attribute.

公开(公告)号：US20250078220A1

公开(公告)日：2025-03-06

申请号：US18458778

申请日：2023-08-30

Applicant: Adobe Inc.

Inventor： Sriram Ravindran ,  Debraj Debashish Basu

IPC: G06T5/00 ,  G06V10/46 ,  G06V10/82

Abstract: In implementation of techniques for generating salient regions based on multi-resolution partitioning, a computing device implements a salient object system to receive a digital image including a salient object. The salient object system generates a first mask for the salient object by partitioning the digital image into salient and non-salient regions. The salient object system also generates a second mask for the salient object that has a resolution that is different than the first mask by partitioning a resampled version of the digital image into salient and non-salient regions. Based on the first mask and the second mask, the salient object system generates an indication of a salient region of the digital image using a machine learning model. The salient object system then displays the indication of the salient region in a user interface.