公开(公告)号：US20200092593A1

公开(公告)日：2020-03-19

申请号：US16694612

申请日：2019-11-25

Applicant: Adobe Inc.

Inventor： Pankhri Singhai ,  Sundeep Parsa ,  Piyush Gupta ,  Nikaash Puri ,  Eshita Shah ,  Balaji Krishnamurthy ,  Nupur Kumari ,  Mayank Singh ,  Akash Rupela

IPC: H04N21/25 ,  H04N21/258 ,  G06Q30/02 ,  H04N21/475 ,  H04N21/81 ,  G06N20/00 ,  H04N21/2668

Abstract: Machine-learning based multi-step engagement strategy generation and visualization is described. Rather than rely heavily on human involvement to create delivery strategies, the described learning-based engagement system generates multi-step engagement strategies by leveraging machine-learning models trained using data describing historical user interactions with content delivered in connection with historical campaigns. Initially, the learning-based engagement system obtains data describing an entry condition and an exit condition for a campaign. Based on the entry and exit condition, the learning-based engagement system utilizes the machine-learning models to generate a multi-step engagement strategy, which describes a sequence of content deliveries that are to be served to a particular client device user (or segment of client device users). Once the multi-step engagement strategies are generated, the learning-based engagement system may also generate visualizations of the strategies that can be output for display.

公开(公告)号：US11989201B2

公开(公告)日：2024-05-21

申请号：US17383009

申请日：2021-07-22

Applicant: Adobe Inc.

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

IPC: G06F16/22 ,  G06F3/0481 ,  G06F16/248 ,  G06F16/26 ,  G06F16/28 ,  G06F18/213 ,  G06F18/2137

CPC classification number: G06F16/26 ,  G06F3/0481 ,  G06F16/2264 ,  G06F16/248 ,  G06F16/283 ,  G06F18/213 ,  G06F18/2137

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：US11481617B2

公开(公告)日：2022-10-25

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 ,  G06N20/00 ,  H04L9/40

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：US20210349915A1

公开(公告)日：2021-11-11

申请号：US17383009

申请日：2021-07-22

Applicant: Adobe Inc.

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

IPC: G06F16/26 ,  G06F16/22 ,  G06F16/28 ,  G06F16/248 ,  G06F3/0481 ,  G06K9/62

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：US11100127B2

公开(公告)日：2021-08-24

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 ,  G06F16/22 ,  G06F16/28 ,  G06F16/248 ,  G06F3/0481 ,  G06K9/62

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：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.