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    MODEL GENERATION TECHNIQUES BASED ON AGGREGATION OF PARTIAL DATA
    1.
    发明申请
    MODEL GENERATION TECHNIQUES BASED ON AGGREGATION OF PARTIAL DATA 有权

    公开(公告)号：US20250086495A1

    公开(公告)日：2025-03-13

    申请号：US18367393

    申请日：2023-09-12

    Applicant: Adobe Inc.

    Inventor： Saayan Mitra ,  Xiang Chen ,  Sapthotharan Krishnan Nair ,  Renzhi Wu ,  Anup Rao

    IPC: G06N20/00

    Abstract: An edge node included in a decentralized edge computing network generates a federated partial-data aggregation machine learning model. The edge node learns one or more model parameters via machine learning techniques and receives one or more auxiliary model parameters from additional edge nodes in the decentralized edge computing network, such as from a neighbor node group. In some cases, a neighbor node is identified in response to determining that the neighbor node includes a model with a relatively high estimated relevance to the model of the edge node. The edge node modifies the model to include an aggregation of the learned model parameters and the received auxiliary parameters. Respective weights are learned for the learned model parameters and also for the received auxiliary parameters. During training to learn the respective weights, the edge node stabilizes the learned model parameters and the received auxiliary parameters.

    GENERATING AND EXECUTING AUTOMATIC SUGGESTIONS TO MODIFY DATA OF INGESTED DATA COLLECTIONS WITHOUT ADDITIONAL DATA INGESTION
    4.
    发明申请
    GENERATING AND EXECUTING AUTOMATIC SUGGESTIONS TO MODIFY DATA OF INGESTED DATA COLLECTIONS WITHOUT ADDITIONAL DATA INGESTION 有权

    公开(公告)号：US20220398230A1

    公开(公告)日：2022-12-15

    申请号：US17347133

    申请日：2021-06-14

    Applicant: Adobe Inc.

    Inventor： Ritwik Sinha ,  Saayan Mitra ,  Handong Zhao ,  Somdeb Sarkhel ,  Trevor Paulsen ,  William Brandon George

    IPC: G06F16/215 ,  G06F16/242 ,  G06N5/04 ,  G06N20/00

    Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating automatic suggestions to effectively modify the organization of an ingested data collection without destruction of the underlying raw data. In particular, in one or more embodiments, the disclosed systems utilize multiple machine learning models in sequence to determine likelihoods that the organizational structure of an ingested data collection should be modified in various ways. In response to generating these likelihoods, the disclosed systems generate corresponding automatic suggestions to modify the organization of the ingested data collection. In response to a detected selection of one or more of the automatic suggestions, the disclosed systems read data out of the ingested data collection in accordance with the selected automatic suggestions to effectively modify the organization of the ingested data collection.

    Online training and update of factorization machines using alternating least squares optimization
    5.
    发明授权
    Online training and update of factorization machines using alternating least squares optimization 有权

    公开(公告)号：US11049041B2

    公开(公告)日：2021-06-29

    申请号：US15963737

    申请日：2018-04-26

    Applicant: Adobe Inc.

    Inventor： Saayan Mitra ,  Xueyu Mao ,  Viswanathan Swaminathan ,  Somdeb Sarkhel ,  Sheng Li

    IPC: G06N20/00 ,  G06F17/16 ,  G06N5/04

    Abstract: Techniques are disclosed for training of factorization machines (FMs) using a streaming mode alternating least squares (ALS) optimization. A methodology implementing the techniques according to an embodiment includes receiving a datapoint that includes a feature vector and an associated target value. The feature vector includes user identification, subject matter identification, and a context. The target value identifies an opinion of the user relative to the subject matter. The method further includes applying an FM to the feature vector to generate an estimate of the target value, and updating parameters of the FM for training of the FM. The parameter update is based on application of a streaming mode ALS optimization to: the datapoint; the estimate of the target value; and to an updated summation of intermediate calculated terms generated by application of the streaming mode ALS optimization to previously received datapoints associated with prior parameter updates of the FM.

    Latency optimization for digital asset compression
    6.
    发明授权
    Latency optimization for digital asset compression 有权

    公开(公告)号：US10942914B2

    公开(公告)日：2021-03-09

    申请号：US15788146

    申请日：2017-10-19

    Applicant: ADOBE INC.

    Inventor： Viswanathan Swaminathan ,  Saayan Mitra

    IPC: G06F16/00 ,  G06F16/23 ,  G06F16/583 ,  G06F16/174

    Abstract: Embodiments of the present disclosure provide systems, methods, and computer storage media for mitigating delays typically experienced when training codebooks during the encoding process. Instead of training a codebook based on a single digital asset, multiple digital assets determined to have asset characteristics in common can be grouped together to form a group of digital assets, from which a single codebook can be trained. The group of digital assets together form a codebook training set, such that each digital asset therein can be analyzed, in parallel, to expeditiously train a single codebook. A codebook trained in this manner can be employed to encode other digital assets sharing the asset characteristics as those in the codebook training set.

    UTILIZING MACHINE LEARNING TO GENERATE PARAMETRIC DISTRIBUTIONS FOR DIGITAL BIDS IN A REAL-TIME DIGITAL BIDDING ENVIRONMENT
    7.
    发明申请
    UTILIZING MACHINE LEARNING TO GENERATE PARAMETRIC DISTRIBUTIONS FOR DIGITAL BIDS IN A REAL-TIME DIGITAL BIDDING ENVIRONMENT 审中-公开

    公开(公告)号：US20200226675A1

    公开(公告)日：2020-07-16

    申请号：US16248287

    申请日：2019-01-15

    Applicant: Adobe Inc.

    Inventor： Saayan Mitra ,  Aritra Ghosh ,  Somdeb Sarkhel ,  Jiatong Xie

    IPC: G06Q30/08 ,  G06N20/00

    Abstract: The present disclosure relates to generating digital bids for providing digital content to remote client devices based on parametric bid distributions generated using a machine learning model (e.g., a mixture density network). For example, in response to identifying a digital bid request in a real-time bidding environment, the disclosed systems can utilize a trained parametric censored machine learning model to generate a parametric bid distribution. To illustrate, the disclosed systems can utilize a parametric censored, mixture density machine learning model to analyze bid request characteristics and generate a parametric, multi-modal distribution reflecting a plurality of parametric means, parametric variances, and combination weights. The disclosed systems can then utilize the parametric, multi-modal distribution to generate digital bids in response to the digital bid request in real-time (e.g., while a client device accesses digital assets corresponding to the bid request).

    GENERATING DIGITAL RECOMMENDATIONS UTILIZING COLLABORATIVE FILTERING, REINFORCEMENT LEARNING, AND INCLUSIVE SETS OF NEGATIVE FEEDBACK
    10.
    发明申请
    GENERATING DIGITAL RECOMMENDATIONS UTILIZING COLLABORATIVE FILTERING, REINFORCEMENT LEARNING, AND INCLUSIVE SETS OF NEGATIVE FEEDBACK 有权

    公开(公告)号：US20230022396A1

    公开(公告)日：2023-01-26

    申请号：US17367134

    申请日：2021-07-02

    Applicant: Adobe Inc.

    Inventor： Saayan Mitra ,  Xiang Chen ,  Vahid Azizi

    IPC: G06Q30/06 ,  G06K9/62

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize collaborative filtering and a reinforcement learning model having an actor-critic framework to provide digital content items across client devices. In particular, in one or more embodiments, the disclosed systems monitor interactions of a client device with one or more digital content items to generate item embeddings (e.g., utilizing a collaborative filtering model). The disclosed systems further utilize a reinforcement learning model to generate a recommendation (e.g., determine one or more additional digital content items to provide to the client device) based on the user interactions. In some implementations, the disclosed systems utilize the reinforcement learning model to analyze every negative and positive interaction observed when generating the recommendation. Further, the disclosed systems utilize the reinforcement learning model to analyze item embeddings, which encode the relationships among the digital content items, when generating the recommendation.

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