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1.发明申请公开(公告)号：US20200097997A1
公开(公告)日：2020-03-26
申请号：US16138403
申请日：2018-09-21
Applicant: Adobe Inc.
Inventor： Sheng Li
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating counterfactuals low-dimensional balanced nonlinear representations for a matching model. For example, the disclosed systems can utilize an ordinal scatter discrepancy model and a maximum mean discrepancy model to generate low-dimensional balanced nonlinear representations of units. In addition, the disclosed systems can generate counterfactuals based on the low-dimensional balanced nonlinear representations by utilizing a matching model. Further, the disclosed systems can determine an average treatment effect on treated units based on the generated counterfactuals.
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2.发明申请公开(公告)号：US20190332971A1
公开(公告)日：2019-10-31
申请号：US15963737
申请日：2018-04-26
Applicant: Adobe Inc.
Inventor： Saayan Mitra , Xueyu Mao , Viswanathan Swaminathan , Somdeb Sarkhel , Sheng Li
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.
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公开(公告)号：US12182713B2
公开(公告)日：2024-12-31
申请号：US16203263
申请日：2018-11-28
Applicant: Adobe Inc.
Inventor： Handong Zhao , Zheng Wen , Sungchul Kim , Sheng Li , Branislav Kveton
Abstract: Systems and techniques for multi-task equidistant embedding are described that process categorical feature data to explore feature interactions. A digital analytics system enforces an equidistant relationship among features within a category while extracting high-order feature interactions by punishing both positive correlations and negative correlations among low-dimensional representations of different features. By enforcing an equidistant embedding, information is retained and accuracy is increased while higher order feature interactions are determined. Further, the digital analytics system shares knowledge among different tasks by connecting a shared network representation common to multiple tasks with exclusive network representations specific to particular tasks.
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公开(公告)号：US11507878B2
公开(公告)日：2022-11-22
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.
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公开(公告)号：US11381651B2
公开(公告)日：2022-07-05
申请号：US16424949
申请日：2019-05-29
Applicant: ADOBE INC.
Inventor： Handong Zhao , Zhiqiang Tao , Zhaowen Wang , Sheng Li , Chen Fang
IPC: H04L29/08 , G06N3/08 , G06N3/04 , H04L67/50 , H04L67/306
Abstract: Methods and systems are provided for generating interpretable user modeling system. The interpretable user modeling system can use an intent neural network to implement one or more tasks. The intent neural network can bridge a semantic gap between log data and human language by leveraging tutorial data to understand user logs in a semantically meaningful way. A memory unit of the intent neural network can capture information from the tutorial data. Such a memory unit can be queried to identify human readable sentences related to actions received by the intent neural network. The human readable sentences can be used to interpret the user log data in a semantically meaningful way.
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Patent Agency Ranking
6.发明授权公开(公告)号：US11373390B2
公开(公告)日：2022-06-28
申请号：US16448473
申请日：2019-06-21
Applicant: Adobe Inc.
Inventor： Handong Zhao , Zhe Lin , Sheng Li , Mingyang Ling , Jiuxiang Gu
IPC: G06V10/26 , G06K9/62 , G06N3/04 , G06N3/08 , G06V10/426
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for generating semantic scene graphs for digital images using an external knowledgebase for feature refinement. For example, the disclosed system can determine object proposals and subgraph proposals for a digital image to indicate candidate relationships between objects in the digital image. The disclosed system can then extract relationships from an external knowledgebase for refining features of the object proposals and the subgraph proposals. Additionally, the disclosed system can generate a semantic scene graph for the digital image based on the refined features of the object/subgraph proposals. Furthermore, the disclosed system can update/train a semantic scene graph generation network based on the generated semantic scene graph. The disclosed system can also reconstruct the image using object labels based on the refined features to further update/train the semantic scene graph generation network.
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公开(公告)号：US20200382612A1
公开(公告)日：2020-12-03
申请号：US16424949
申请日：2019-05-29
Applicant: ADOBE INC.
Inventor： Handong Zhao , Zhiqiang Tao , Zhaowen Wang , Sheng Li , Chen Fang
Abstract: Methods and systems are provided for generating interpretable user modeling system. The interpretable user modeling system can use an intent neural network to implement one or more tasks. The intent neural network can bridge a semantic gap between log data and human language by leveraging tutorial data to understand user logs in a semantically meaningful way. A memory unit of the intent neural network can capture information from the tutorial data. Such a memory unit can be queried to identify human readable sentences related to actions received by the intent neural network. The human readable sentences can be used to interpret the user log data in a semantically meaningful way.
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8.发明授权公开(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
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.
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公开(公告)号：US20200167690A1
公开(公告)日：2020-05-28
申请号：US16203263
申请日：2018-11-28
Applicant: Adobe Inc.
Inventor： Handong Zhao , Zheng Wen , Sungchul Kim , Sheng Li , Branislav Kveton
Abstract: Systems and techniques for multi-task equidistant embedding are described that process categorical feature data to explore feature interactions. A digital analytics system enforces an equidistant relationship among features within a category while extracting high-order feature interactions by punishing both positive correlations and negative correlations among low-dimensional representations of different features. By enforcing an equidistant embedding, information is retained and accuracy is increased while higher order feature interactions are determined. Further, the digital analytics system shares knowledge among different tasks by connecting a shared network representation common to multiple tasks with exclusive network representations specific to particular tasks.
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10.发明申请公开(公告)号：US20190325068A1
公开(公告)日：2019-10-24
申请号：US15957556
申请日：2018-04-19
Applicant: Adobe Inc.
Inventor： Tuan Manh Lai , Trung Bui , Sheng Li , Quan Hung Tran , Hung Bui
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating digital responses to digital queries by utilizing a classification model and query-specific analysis models. For example, the disclosed systems can train a classification model to generate query classifications corresponding to product queries, conversational queries, and/or recommendation/purchase queries. Moreover, the disclosed systems can apply the classification model to select pertinent models for particular queries. For example, upon classifying a product query, disclosed systems can utilize a neural ranking model (trained based on a set of training product specifications and training queries) to generate relevance scores for product specifications associated with a digital query. The disclosed systems can further compare generated relevance scores to select a product specification and generate a digital response that includes the pertinent product specification to provide for display to a client device.
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