公开(公告)号：US20200327446A1

公开(公告)日：2020-10-15

申请号：US16380566

申请日：2019-04-10

Applicant: Adobe Inc.

Inventor： Xiaowei Jia ,  Sheng Li ,  Handong Zhao ,  Sungchul Kim

IPC: G06N20/00

Abstract: Techniques are disclosed for the generation of adversarial training data through sequence perturbation, for a deep learning network to perform event sequence analysis. A methodology implementing the techniques according to an embodiment includes applying a long short-term memory attention model to an input data sequence to generate discriminative sequence periods and attention weights associated with the discriminative sequence periods. The attention weights are generated to indicate the relative importance of data in those discriminative sequence periods. The method further includes generating perturbed data sequences based on the discriminative sequence periods and the attention weights. The generation of the perturbed data sequences employs selective filtering or conservative adversarial training, to preserve perceptual similarity between the input data sequence and the perturbed data sequences. The input data sequence may be created by vectorizing a temporal input data stream comprising words, symbols, and the like, into a multidimensional vectorized numerical data sequence format.

公开(公告)号：US10728104B2

公开(公告)日：2020-07-28

申请号：US16192313

申请日：2018-11-15

Applicant: Adobe Inc.

Inventor： Ryan A. Rossi ,  Sungchul Kim ,  Eunyee Koh

IPC: H04L12/24 ,  H04L12/26 ,  H04L12/751 ,  H04L29/08

Abstract: In implementations of time-dependent network embedding, a computing device maintains time-dependent interconnected data in the form of a time-based graph that includes nodes and node associations that each represent an edge between two of the nodes in the time-based graph based at least in part on a temporal value that indicates when the two nodes were associated. The computing device includes a network embedding module that is implemented to traverse one or more of the nodes in the time-based graph along the node associations, where the traversal is performed with respect to the temporal value of each of the edges that associate the nodes. The network embedding module is also implemented to determine a time-dependent embedding for each of the nodes traversed in the time-based graph, the time-dependent embedding for each of the respective nodes being representative of feature values that describe the respective node.

公开(公告)号：US10558852B2

公开(公告)日：2020-02-11

申请号：US15814979

申请日：2017-11-16

Applicant: ADOBE INC.

Inventor： Sungchul Kim ,  Deepali Jain ,  Deepali Gupta ,  Eunyee Koh ,  Branislav Kveton ,  Nikhil Sheoran ,  Atanu Sinha ,  Hung Hai Bui ,  Charles Li Chen

IPC: G06K9/00 ,  G06N3/04 ,  G06N3/08 ,  G06F16/954 ,  G06K9/62

Abstract: Systems and methods provide for generating predictive models that are useful in predicting next-user-actions. User-specific navigation sequences are obtained, the navigation sequences representing temporally-related series of actions performed by users during navigation sessions. To each navigation sequence, a Recurrent Neural Network (RNN) is applied to encode the navigation sequences into user embeddings that reflect time-based, sequential navigation patterns for the user. Once a set of navigation sequences is encoded to a set of user embeddings, a variety of classifiers (prediction models) may be applied to the user embeddings to predict what a probable next-user-action may be and/or the likelihood that the next-user-action will be a desired target action.