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    Generating combined feature embedding for minority class upsampling in training machine learning models with imbalanced samples
    11.
    发明授权
    Generating combined feature embedding for minority class upsampling in training machine learning models with imbalanced samples 有权

    公开(公告)号：US11631029B2

    公开(公告)日：2023-04-18

    申请号：US16564531

    申请日：2019-09-09

    Applicant: Adobe, Inc.

    Inventor： Nikaash Puri ,  Balaji Krishnamurthy ,  Ayush Chopra

    IPC: G06N20/00 ,  G06N5/04

    Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for generating combined feature embeddings for minority class upsampling in training machine learning models with imbalanced training samples. For example, the disclosed systems can select training sample values from a set of training samples and a combination ratio value from a continuous probability distribution. Additionally, the disclosed systems can generate a combined synthetic training sample value by modifying the selected training sample values using the combination ratio value and combining the modified training sample values. Moreover, the disclosed systems can generate a combined synthetic ground truth label based on the combination ratio value. In addition, the disclosed systems can utilize the combined synthetic training sample value and the combined synthetic ground truth label to generate a combined synthetic training sample and utilize the combined synthetic training sample to train a machine learning model.

    Machine-learning based multi-step engagement strategy modification
    12.
    发明授权
    Machine-learning based multi-step engagement strategy modification 有权

    公开(公告)号：US11107115B2

    公开(公告)日：2021-08-31

    申请号：US16057743

    申请日：2018-08-07

    Applicant: Adobe Inc.

    Inventor： Pankhri Singhai ,  Sundeep Parsa ,  Piyush Gupta ,  Nupur Kumari ,  Nikaash Puri ,  Mayank Singh ,  Eshita Shah ,  Balaji Krishnamurthy ,  Akash Rupela

    IPC: G06Q30/00 ,  G06Q30/02 ,  G06N20/00 ,  G05B19/418

    Abstract: Machine-learning based multi-step engagement strategy modification is described. Rather than rely heavily on human involvement to manage content delivery over the course of a campaign, the described learning-based engagement system modifies a multi-step engagement strategy, originally created by an engagement-system user, by leveraging machine-learning models. In particular, these leveraged machine-learning models are trained using data describing user interactions with delivered content as those interactions occur over the course of the campaign. Initially, the learning-based engagement system obtains a multi-step engagement strategy created by an engagement-system user. As the multi-step engagement strategy is deployed, the learning-based engagement system randomly adjusts aspects of the sequence of deliveries for some users. Based on data describing the interactions of recipients with deliveries served according to both the user-created and random multi-step engagement strategies, the machine-learning models generate a modified multi-step engagement strategy.

    Entropy Based Synthetic Data Generation For Augmenting Classification System Training Data
    13.
    发明申请
    Entropy Based Synthetic Data Generation For Augmenting Classification System Training Data 有权

    公开(公告)号：US20210117718A1

    公开(公告)日：2021-04-22

    申请号：US16659147

    申请日：2019-10-21

    Applicant: Adobe Inc.

    Inventor： Pinkesh Badjatiya ,  Nikaash Puri ,  Ayush Chopra ,  Anubha Kabra

    IPC: G06K9/62 ,  G06N20/10

    Abstract: A data classification system is trained to classify input data into multiple classes. The system is initially trained by adjusting weights within the system based on a set of training data that includes multiple tuples, each being a training instance and corresponding training label. Two training instances, one from a minority class and one from a majority class, are selected from the set of training data based on entropies for the training instances. A synthetic training instance is generated by combining the two selected training instances and a corresponding training label is generated. A tuple including the synthetic training instance and the synthetic training label is added to the set of training data, resulting in an augmented training data set. One or more such synthetic training instances can be added to the augmented training data set and the system is then re-trained on the augmented training data set.

    Rule Determination for Black-Box Machine-Learning Models
    16.
    发明申请
    Rule Determination for Black-Box Machine-Learning Models 审中-公开

    公开(公告)号：US20190147369A1

    公开(公告)日：2019-05-16

    申请号：US15812991

    申请日：2017-11-14

    Applicant: Adobe Inc.

    Inventor： Piyush Gupta ,  Sukriti Verma ,  Pratiksha Agarwal ,  Nikaash Puri ,  Balaji Krishnamurthy

    IPC: G06N99/00 ,  G06N5/02

    Abstract: Rule determination for black-box machine-learning models (BBMLMs) is described. These rules are determined by an interpretation system to describe operation of a BBMLM to associate inputs to the BBMLM with observed outputs of the BBMLM and without knowledge of the logic used in operation by the BBMLM to make these associations. To determine these rules, the interpretation system initially generates a proxy black-box model to imitate the behavior of the BBMLM based solely on data indicative of the inputs and observed outputs—since the logic actually used is not available to the system. The interpretation system generates rules describing the operation of the BBMLM by combining conditions—identified based on output of the proxy black-box model—using a genetic algorithm. These rules are output as if-then statements configured with an if-portion formed as a list of the conditions and a then-portion having an indication of the associated observed output.

    DIGITAL EXPERIENCE TARGETING USING BAYESIAN APPROACH
    17.
    发明申请
    DIGITAL EXPERIENCE TARGETING USING BAYESIAN APPROACH 审中-公开

    公开(公告)号：US20190114673A1

    公开(公告)日：2019-04-18

    申请号：US15787369

    申请日：2017-10-18

    Applicant: Adobe Inc.

    Inventor： Piyush Gupta ,  Nikaash Puri ,  Balaji Krishnamurthy

    IPC: G06Q30/02 ,  G06N7/00 ,  G06N5/02 ,  G06N3/02

    Abstract: Digital experience targeting techniques are disclosed which serve digital experiences that have a high probability of conversion with regard to a given user visit profile. In some examples, a method may include predicting a probability of each digital experience in a campaign being served based on a user visit profile and an indication that a user exhibiting the user visit profile is going to convert, predicting a probability of each digital experience in the campaign being served based on the user visit profile and an indication that the user exhibiting the user visit profile is not going to convert, and deriving, for the user visit profile, a probability of conversion for each digital experience in the campaign. The probability of conversion for each digital experience in the campaign for the user visit profile may be derived using a Bayesian framework.

    Spoken query processing for image search
    18.
    发明授权
    Spoken query processing for image search 有权

    公开(公告)号：US12288549B2

    公开(公告)日：2025-04-29

    申请号：US17887959

    申请日：2022-08-15

    Applicant: Adobe Inc.

    Inventor： Ajay Jain ,  Sanjeev Tagra ,  Sachin Soni ,  Ryan Rozich ,  Nikaash Puri ,  Jonathan Roeder

    IPC: G10L15/06 ,  G06F3/16 ,  G06F16/532 ,  G06F40/284 ,  G06F40/30 ,  G06V10/774 ,  G10L15/183 ,  G10L15/22

    Abstract: An image search system uses a multi-modal model to determine relevance of images to a spoken query. The multi-modal model includes a spoken language model that extracts features from spoken query and a language processing model that extract features from an image. The multi-model model determines a relevance score for the image and the spoken query based on the extracted features. The multi-modal model is trained using a curriculum approach that includes training the spoken language model using audio data. Subsequently, a training dataset comprising a plurality of spoken queries and one or more images associated with each spoken query is used to jointly train the spoken language model and an image processing model to provide a trained multi-modal model.

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