LATENT NETWORK SUMMARIZATION
    41.
    发明申请

    公开(公告)号:US20200233864A1

    公开(公告)日:2020-07-23

    申请号:US16252169

    申请日:2019-01-18

    申请人: ADOBE INC.

    摘要: Embodiments of the present invention provide systems, methods, and computer storage media for latent summarization of a graph. Structural features can be captured from feature vectors associated with each node of the graph by applying base functions on the feature vectors and iteratively applying relational operators to successive feature matrices to derive deeper inductive relational functions that capture higher-order structural information in different subgraphs of increasing size (node separations). Heterogeneity can be summarized by performing capturing features in appropriate subgraphs (e.g., node-centric neighborhoods associated with each node type, edge direction, and/or edge type). Binning and/or dimensionality reduction can be applied to the resulting feature matrices. The resulting set of relational functions and multi-level feature matrices can form a latent summary that can be used to perform a variety of graph-based tasks, including node classification, node clustering, link prediction, entity resolution, anomaly and event detection, and inductive learning tasks.

    Higher-Order Network Embedding
    42.
    发明申请

    公开(公告)号:US20200177466A1

    公开(公告)日:2020-06-04

    申请号:US16204616

    申请日:2018-11-29

    申请人: Adobe Inc.

    摘要: In implementations of higher-order network embedding, a computing device maintains interconnected data in the form of a graph that represents a network, the graph including nodes that each represent entities in the network and node associations that each represent edges between the nodes in the graph. The computing device includes a network embedding module that is implemented to determine a frequency of k-vertex motifs for each of the edges in the graph, and derive motif-based matrices from the frequency of each of the k-vertex motifs in the graph. The network embedding module is also implemented to determine a higher-order network embedding for each of the nodes in the graph from each of the motif-based matrices. The network embedding module can then concatenate the higher-order network embeddings into a matrix representation.

    BUILDING TIME-DECAYED LINE GRAPHS FOR DIRECT EMBEDDING OF CONTINUOUS-TIMED INTERACTIONS IN GENERATING TIME-AWARE RECOMMENDATIONS

    公开(公告)号:US20240311623A1

    公开(公告)日:2024-09-19

    申请号:US18183387

    申请日:2023-03-14

    申请人: Adobe Inc.

    IPC分类号: G06N3/049

    CPC分类号: G06N3/049

    摘要: The present disclosure relates to systems, non-transitory computer-readable media, and methods for building time-decayed line graphs from temporal graph networks for efficiently and accurately generating time-aware recommendations. For example, the time-decayed line graph system creates a line graph of the temporal graph network by deriving interaction nodes from temporal edges (e.g., timed interactions) and connecting interactions that share an endpoint node. Then, the time-decayed line graph system determines the edge weights in the line graph based on differences in time between interactions, with interactions that occur closer together in time being connected with higher weights. Notably, by using this method, the derived time-decayed line graph directly represents topological proximity and temporal proximity. Upon generating the time-decayed line graphs, the system performs downstream predictive modeling such as predicted edge classifications and/or temporal link predictions.

    Utilizing a graph neural network to generate visualization and attribute recommendations

    公开(公告)号:US12093322B2

    公开(公告)日:2024-09-17

    申请号:US17654933

    申请日:2022-03-15

    申请人: Adobe Inc.

    IPC分类号: G06F16/904 G06N3/02

    CPC分类号: G06F16/904 G06N3/02

    摘要: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize a graph neural network to generate data recommendations. The disclosed systems generate a digital graph representation comprising user nodes corresponding to users, data attribute nodes corresponding to data attributes, and edges reflecting historical interactions between the users and the data attributes; Moreover, the disclosed systems generate, utilizing a graph neural network, user embeddings for the user nodes and data attribute embeddings for the data attribute nodes from the digital graph representation. In addition, the disclosed systems generate, utilizing a graph neural network, user embeddings for the user nodes and data attribute embeddings for the data attribute nodes from the digital graph representation. Furthermore, the disclosed systems determine a data recommendation for a target user utilizing the data attribute embeddings and a target user embedding corresponding to the target user from the user embeddings.

    Teaching a machine classifier to recognize a new class

    公开(公告)号:US11995403B2

    公开(公告)日:2024-05-28

    申请号:US17524282

    申请日:2021-11-11

    申请人: ADOBE INC.

    IPC分类号: G06F40/295 G06N20/00

    CPC分类号: G06F40/295 G06N20/00

    摘要: Embodiments of the technology described herein describe a machine classifier capable of continually learning new classes through a continual few-shot learning approach. A natural language processing (NLP) machine classifier may initially be trained to identify a plurality of other classes through a conventional training process. In order to learn a new class, natural-language training data for a new class is generated. The training data for the new class may be few-shot training data. The training also uses synthetic training data that represents each of the plurality of other classes. The synthetic training data may be generated through a model inversion of the original classifier. The synthetic training data and the natural-language training data are used to retrain the NLP classifier to identify text in the plurality of other classes and the new class using.

    AUTOMATIC FORECASTING USING META-LEARNING
    48.
    发明公开

    公开(公告)号:US20240152769A1

    公开(公告)日:2024-05-09

    申请号:US18050607

    申请日:2022-10-28

    申请人: ADOBE INC.

    IPC分类号: G06N3/0985 G06Q10/04

    CPC分类号: G06N3/0985 G06Q10/04

    摘要: Systems and methods for automatic forecasting are described. Embodiments of the present disclosure receive a time-series dataset; compute a time-series meta-feature vector based on the time-series dataset; generate a performance score for a forecasting model using a meta-learner machine learning model that takes the time-series meta-feature vector as input; select the forecasting model from a plurality of forecasting models based on the performance score; and generate predicted time-series data based on the time-series dataset using the selected forecasting model.

    Configuration of user interface for intuitive selection of insight visualizations

    公开(公告)号:US11782576B2

    公开(公告)日:2023-10-10

    申请号:US17161770

    申请日:2021-01-29

    申请人: Adobe Inc.

    摘要: In some embodiments, a data visualization system detects insights from a dataset and computes insight scores for respective insights. The data visualization system further computes insight type scores, from the insight scores, for insight types in the detected insights. The data visualization system determines a selected insight type for the dataset having a higher insight type score than unselected insight types and determines, for the selected insight type, a set of selected insights that have higher insight scores than unselected insights. The data visualization system determines insight visualizations for the set of selected insights and generates, for inclusion in a user interface of the data visualization system, selectable interface elements configured for invoking an editing tool for updating the determined insight visualizations from the dataset. The selectable interface elements are arranged in the user interface according to the insight scores of the set of selected insights.

    ENHANCING NEURAL-BASED PREDICTION OF MULTI-DIMENSIONAL DATA VIA INFLUENCE AND DATA AUGMENTATION

    公开(公告)号:US20230244926A1

    公开(公告)日:2023-08-03

    申请号:US17592186

    申请日:2022-02-03

    申请人: ADOBE INC.

    IPC分类号: G06N3/08

    CPC分类号: G06N3/08

    摘要: A data augmentation framework enhances the prediction accuracy of tensor completion methods. An array having a set of cells associated with a set of entities is received. Influence metrics of cells from the array are determined based on an influence of the cells on minimizing loss while training a machine learning model. An entity-importance metric is generated for each entity of the set of entities based on the influence metrics. A cell from the array for which to augment the array with a predicted value is identified. The cell is identified based on a sampling of the set of entities that is weighted by the entity-importance metric for each entity of the set of entities.