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    Online inference and learning for nonsymmetric determinantal point processes
    1.
    发明授权
    Online inference and learning for nonsymmetric determinantal point processes 有权

    公开(公告)号：US12288237B2

    公开(公告)日：2025-04-29

    申请号：US17743360

    申请日：2022-05-12

    Applicant: Adobe Inc.

    Inventor： Ryan A. Rossi ,  Aravind Reddy Talla ,  Zhao Song ,  Anup Rao ,  Tung Mai ,  Nedim Lipka ,  Gang Wu ,  Eunyee Koh

    IPC: G06Q30/0601

    Abstract: Embodiments provide systems, methods, and computer storage media for a Nonsymmetric Determinantal Point Process (NDPPs) for compatible set recommendations in a setting where data representing entities (e.g., items) arrives in a stream. A stream representing compatible sets of entities is received and used to update a latent representation of the entities and a compatibility distribution indicating likelihood of compatibility of subsets of the entities. The probability distribution is accessed in a single sequential pass to predict a compatible complete set of entities that completes an incomplete set of entities. The predicted complete compatible set is provided a recommendation for entities that complete the incomplete set of entities.

    Systems and methods for estimating typed graphlets in large data
    3.
    发明授权
    Systems and methods for estimating typed graphlets in large data 有权

    公开(公告)号：US11343325B2

    公开(公告)日：2022-05-24

    申请号：US17008339

    申请日：2020-08-31

    Applicant: Adobe Inc.

    Inventor： Ryan Rossi ,  Tung Mai ,  Anup Rao

    IPC: G06F15/173 ,  H04L67/141 ,  G06F17/18 ,  G06F16/901

    Abstract: A system and method for fast, accurate, and scalable typed graphlet estimation. The system and method utilizes typed edge sampling and typed path sampling to estimate typed graphlet counts in large graphs in a small fraction of the computing time of existing systems. The obtained unbiased estimates of typed graphlets are highly accurate, and have applications in the analysis, mining, and predictive modeling of massive real-world networks. During operation, the system obtains a dataset indicating nodes and edges of a graph. The system samples a portion of the graph and counts a number of graph features in the sampled portion of the graph. The system then computes an occurrence frequency of a typed graphlet pattern and a total number of typed graphlets associated with the typed graphlet pattern in the graph.

    DETERMINING PATTERNS WITHIN A STRING SEQUENCE OF USER ACTIONS
    4.
    发明申请
    DETERMINING PATTERNS WITHIN A STRING SEQUENCE OF USER ACTIONS 有权

    公开(公告)号：US20220148015A1

    公开(公告)日：2022-05-12

    申请号：US17096255

    申请日：2020-11-12

    Applicant: Adobe Inc.

    Inventor： Tung Mai ,  Iftikhar Ahamath Burhanuddin ,  Georgios Theocharous ,  Anup Rao

    IPC: G06Q30/02 ,  G06F7/08 ,  G06Q10/10

    Abstract: Techniques are provided for analyzing user actions that have occurred over a time period. The user actions can be, for example, with respect to the user's navigation of content or interaction with an application. Such user data is provided in an action string, which is converted into a highly searchable format. As such, the presence and frequency of particular user actions and patterns of user actions within an action string of a particular user, as well as among multiple action strings of multiple users, are determinable. Subsequences of one or more action strings are identified and both the number of action strings that include a particular subsequence and the frequency that a particular subsequence is present in a given action string are determinable. The conversion involves breaking that string into a sorted list of locations for the actions within that string. Queries can be readily applied against the sorted list.

    GENERATING OVERLAP ESTIMATIONS BETWEEN HIGH-VOLUME DIGITAL DATA SETS BASED ON MULTIPLE SKETCH VECTOR SIMILARITY ESTIMATORS
    5.
    发明申请
    GENERATING OVERLAP ESTIMATIONS BETWEEN HIGH-VOLUME DIGITAL DATA SETS BASED ON MULTIPLE SKETCH VECTOR SIMILARITY ESTIMATORS 有权

    公开(公告)号：US20220138218A1

    公开(公告)日：2022-05-05

    申请号：US17090556

    申请日：2020-11-05

    Applicant: Adobe Inc.

    Inventor： Anup Rao ,  Tung Mai ,  Matvey Kapilevich

    IPC: G06F16/26 ,  G06F16/28 ,  G06T11/20

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that estimate the overlap between sets of data samples. In particular, in one or more embodiments, the disclosed systems utilize a sketch-based sampling routine and a flexible, accurate estimator to determine the overlap (e.g., the intersection) between sets of data samples. For example, in some implementations, the disclosed systems generate a sketch vector—such as a one permutation hashing vector—for each set of data samples. The disclosed systems further compare the sketch vectors to determine an equal bin similarity estimator, a lesser bin similarity estimator, and a greater bin similarity estimator. The disclosed systems utilize one or more of the determined similarity estimators in generating an overlap estimation for the sets of data samples.

    Trait Expansion Techniques in Binary Matrix Datasets
    7.
    发明公开
    Trait Expansion Techniques in Binary Matrix Datasets 审中-公开

    公开(公告)号：US20230267132A1

    公开(公告)日：2023-08-24

    申请号：US17677323

    申请日：2022-02-22

    Applicant: Adobe Inc.

    Inventor： Yeuk-yin Chan ,  Tung Mai ,  Ryan Rossi ,  Moumita Sinha ,  Matvey Kapilevich ,  Margarita Savova ,  Fan Du ,  Charles Menguy ,  Anup Rao

    IPC: G06F16/28

    CPC classification number: G06F16/285

    Abstract: A cluster generation system identifies data elements, from a first binary record, that each have a particular value and correspond to respective binary traits. A candidate description function describing the binary traits is generated, the candidate description function including a model factor that describes the data elements. Responsive to determining that a second record has additional data elements having the particular value and corresponding to the respective binary traits, the candidate description function is modified to indicate that the model factor describes the additional elements. The candidate description function is also modified to include a correction factor describing an additional binary trait excluded from the respective binary traits. Based on the modified candidate description function, the cluster generation system generates a data summary cluster, which includes a compact representation of the binary traits of the data elements and additional data elements.

    MACHINE UNLEARNING AND RETRAINING OF A MACHINE LEARNING MODEL BASED ON A MODIFIED TRAINING DATASET
    8.
    发明申请
    MACHINE UNLEARNING AND RETRAINING OF A MACHINE LEARNING MODEL BASED ON A MODIFIED TRAINING DATASET 有权

    公开(公告)号：US20230118785A1

    公开(公告)日：2023-04-20

    申请号：US17451260

    申请日：2021-10-18

    Applicant: ADOBE INC.

    Inventor： Enayat Ullah ,  Anup Bandigadi Rao ,  Tung Mai ,  Ryan A. Rossi

    IPC: G06N3/08 ,  G06N3/04

    Abstract: Systems and methods for training a neural network are described. One or more embodiments of the present disclosure include training a neural network based on a first combined gradient of a loss function at a plurality of sampled elements of a dataset; receiving an insertion request that indicates an insertion element to be added to the dataset, or a deletion request that indicates a deletion element to be removed from the dataset, wherein the deletion element is one of the plurality of sampled elements; computing a second combined gradient of the loss function by adding the insertion element to the dataset or by replacing the deletion element with a replacement element from the dataset; determining whether the first combined gradient and the second combined gradient satisfy a stochastic condition; and retraining the neural network to obtain a modified neural network based on the determination.

    Generating overlap estimations between high-volume digital data sets based on multiple sketch vector similarity estimators
    9.
    发明授权
    Generating overlap estimations between high-volume digital data sets based on multiple sketch vector similarity estimators 有权

    公开(公告)号：US11449523B2

    公开(公告)日：2022-09-20

    申请号：US17090556

    申请日：2020-11-05

    Applicant: Adobe Inc.

    Inventor： Anup Rao ,  Tung Mai ,  Matvey Kapilevich

    IPC: G06F16/00 ,  G06F16/26 ,  G06T11/20 ,  G06F16/28

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that estimate the overlap between sets of data samples. In particular, in one or more embodiments, the disclosed systems utilize a sketch-based sampling routine and a flexible, accurate estimator to determine the overlap (e.g., the intersection) between sets of data samples. For example, in some implementations, the disclosed systems generate a sketch vector—such as a one permutation hashing vector—for each set of data samples. The disclosed systems further compare the sketch vectors to determine an equal bin similarity estimator, a lesser bin similarity estimator, and a greater bin similarity estimator. The disclosed systems utilize one or more of the determined similarity estimators in generating an overlap estimation for the sets of data samples.

    Utilizing one hash permutation and populated-value-slot-based densification for generating audience segment trait recommendations
    10.
    发明授权
    Utilizing one hash permutation and populated-value-slot-based densification for generating audience segment trait recommendations 有权

    公开(公告)号：US11109085B2

    公开(公告)日：2021-08-31

    申请号：US16367628

    申请日：2019-03-28

    Applicant: Adobe Inc.

    Inventor： Anup Rao ,  Yasin Abbasi Yadkori ,  Tung Mai ,  Ryan Rossi ,  Ritwik Sinha ,  Matvey Kapilevich ,  Alexandru Ionut Hodorogea

    IPC: G06F7/00 ,  G06F16/00 ,  H04N21/258 ,  H04N21/482 ,  H04N21/2668

    Abstract: The present disclosure relates to training a recommendation model to generate trait recommendations using one permutation hashing and populated-value-slot-based densification. In particular, the disclosed systems can train the recommendation model by computing sketch vectors corresponding to traits using one permutation hashing. The disclosed systems can then fill in unpopulated value slots of the sketch vectors using populated-value-slot-based densification. The disclosed systems can combine the resulting densified sketches to generate the trained recommendation model. For example, in some embodiments, the disclosed systems can combine the sketches by generating a plurality of locality sensitive hashing tables based on the sketches. In some embodiments, the disclosed systems generate a count sketch matrix based on the sketches and generate trait embeddings based on the count sketch matrix using spectral embedding. Based on the trait embeddings, the disclosed systems can utilize the recommendation model to flexibly and accurately determine the similarity between traits.

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