公开(公告)号：US20200320381A1

公开(公告)日：2020-10-08

申请号：US16375037

申请日：2019-04-04

Applicant: ADOBE INC.

Inventor： Vaidyanathan Venkatraman ,  Rajan Madhavan ,  Omar Rahman ,  Niranjan Shivanand Kumbi ,  Brajendra Kumar Bhujabal ,  Ajay Awatramani

IPC: G06N3/08 ,  G06Q10/06

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for providing factors that explain the generated results of a deep neural network (DNN). In embodiments, multiple machine learning models and a DNN are trained on a training dataset. A preliminary set of trained machine learning models with similar results to the trained DNN are selected for further evaluation. The preliminary set of machine learning models may be evaluated using a distribution analysis to select a reduced set of machine learning models. Results produced by the reduced set of machine learning models are compared, point-by-point, to the results produced by the DNN. The best performing machine learning model with generated results that performs closest to the DNN generated results may be selected. One or more factors used by the selected machine learning model are determined. Those one or more factors from the selected best performing machine learning model may be provided to explain the results of the DNN and increase confidence in the understanding and accuracy of the results generated by the DNN.

公开(公告)号：US11775813B2

公开(公告)日：2023-10-03

申请号：US16446386

申请日：2019-06-19

Applicant: Adobe Inc.

Inventor： Niranjan Kumbi ,  Vaidyanathan Venkatraman ,  Rajan Madhavan ,  Omar Rahman ,  Kai Lau ,  Badsah Mukherji ,  Ajay Awatramani

IPC: G06Q10/04 ,  G06Q30/0202 ,  G06N3/08 ,  G06N20/00

CPC classification number: G06N3/08 ,  G06Q10/04 ,  G06Q30/0202 ,  G06N20/00

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating a recommended target audience based on determining a predicted attendance utilizing a neural network approach. For example, the disclosed systems can utilize an approximate nearest neighbor algorithm to identify individuals that are within a similarity threshold of invitees for an event. In addition, the disclosed systems can implement an attendance prediction model to determine a probability of an invitee attending the event. The disclosed systems can further determine a predicted attendance for an event based on the individual probabilities. Based on identifying the similar individuals to, and the attendance probabilities for, the invitees, the disclosed systems can generate a recommended target audience to satisfy a target attendance for an event based on a predicted attendance for the event.

公开(公告)号：US11501161B2

公开(公告)日：2022-11-15

申请号：US16375037

申请日：2019-04-04

Applicant: ADOBE INC.

Inventor： Vaidyanathan Venkatraman ,  Rajan Madhavan ,  Omar Rahman ,  Niranjan Shivanand Kumbi ,  Brajendra Kumar Bhujabal ,  Ajay Awatramani

IPC: G06K9/62 ,  G06N3/04 ,  G06N3/08 ,  G06Q10/06

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for providing factors that explain the generated results of a deep neural network (DNN). In embodiments, multiple machine learning models and a DNN are trained on a training dataset. A preliminary set of trained machine learning models with similar results to the trained DNN are selected for further evaluation. The preliminary set of machine learning models may be evaluated using a distribution analysis to select a reduced set of machine learning models. Results produced by the reduced set of machine learning models are compared, point-by-point, to the results produced by the DNN. The best performing machine learning model with generated results that performs closest to the DNN generated results may be selected. One or more factors used by the selected machine learning model are determined. Those one or more factors from the selected best performing machine learning model may be provided to explain the results of the DNN and increase confidence in the understanding and accuracy of the results generated by the DNN.