公开(公告)号：US20200311100A1

公开(公告)日：2020-10-01

申请号：US16368415

申请日：2019-03-28

Applicant: Adobe Inc.

Inventor： Akash Rupela ,  Piyush Gupta ,  Nupur Kumari ,  Bishal Deb ,  Balaji Krishnamurthy ,  Ankita Sarkar

IPC: G06F16/26 ,  G06F16/248 ,  G06F16/28 ,  G06F16/22

Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that generate and render a varied-scale-topological construct for a multidimensional dataset to visually represent portions of the multidimensional dataset at different topological scales. In certain implementations, for example, the disclosed systems generate and combine (i) an initial topological construct for a multidimensional dataset at one scale and (ii) a local topological construct for a subset of the multidimensional dataset at another scale to form a varied-scale-topological construct. To identify a region from an initial topological construct to vary in scale, the disclosed systems can determine the relative densities of subsets of multidimensional data corresponding to regions of the initial topological construct and select one or more such regions to change in scale.

公开(公告)号：US20200234110A1

公开(公告)日：2020-07-23

申请号：US16253561

申请日：2019-01-22

Applicant: Adobe Inc.

Inventor： Mayank Singh ,  Nupur Kumari ,  Dhruv Khattar ,  Balaji Krishnamurthy ,  Abhishek Sinha

IPC: G06N3/08 ,  H04L29/06 ,  G06N20/00

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating trained neural network with increased robustness against adversarial attacks by utilizing a dynamic dropout routine and/or a cyclic learning rate routine. For example, the disclosed systems can determine a dynamic dropout probability distribution associated with neurons of a neural network. The disclosed systems can further drop neurons from a neural network based on the dynamic dropout probability distribution to help neurons learn distinguishable features. In addition, the disclosed systems can utilize a cyclic learning rate routine to force copy weights of a copy neural network away from weights of an original neural network without decreasing prediction accuracy to ensure that the decision boundaries learned are different.

公开(公告)号：US20190138917A1

公开(公告)日：2019-05-09

申请号：US16241456

申请日：2019-01-07

Applicant: Adobe Inc.

Inventor： Balaji Krishnamurthy ,  Tushar Singla

IPC: G06N5/02 ,  G06F16/9535 ,  G06Q30/02 ,  G06F16/901

Abstract: Behavioral prediction for targeted end users is described. In one or more example embodiments, a computer-readable storage medium has multiple instructions that cause one or more processors to perform multiple operations. Targeted selectstream data is obtained from one or more indications of data object requests corresponding to a targeted end user. A targeted directed graph is constructed based on the targeted selectstream data. A targeted graph feature vector is computed based on one or more invariant features associated with the targeted directed graph. A behavioral prediction is produced for the targeted end user by applying a prediction model to the targeted graph feature vector. In one or more example embodiments, the prediction model is generated based on multiple graph feature vectors respectively corresponding to multiple end users. In one or more example embodiments, a tailored opportunity is determined responsive to the behavioral prediction and issued to the targeted end user.