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

IPC: G06N20/00 ,  G06N3/08 ,  G06N7/00 ,  G06K9/62 ,  G06F9/48

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.

公开(公告)号：US20250061609A1

公开(公告)日：2025-02-20

申请号：US18451201

申请日：2023-08-17

Applicant: ADOBE INC.

Inventor： Junda Wu ,  Haoliang Wang ,  Tong Yu ,  Stefano Petrangeli ,  Gang Wu ,  Viswanathan Swaminathan ,  Sungchul Kim ,  Handong Zhao

IPC: G06T9/40 ,  G06V10/70 ,  G06V10/75

Abstract: One or more aspects of the method, apparatus, and non-transitory computer readable medium include obtaining image data and computing a prediction residue value for a pixel of the image data using a prediction function. An entropy value for the pixel can then be determined based on the prediction residue value using context modeling, and progressive compressed image data for the image data can be generated based on the entropy value. The compressed image data can be used to enable collaborative image editing and other image processing tasks.

公开(公告)号：US20240404243A1

公开(公告)日：2024-12-05

申请号：US18328950

申请日：2023-06-05

Applicant: ADOBE INC.

Inventor： Handong Zhao ,  Yue Bai ,  Zhe Lin ,  Ajinkya Gorakhnath Kale ,  Jiuxiang Gu ,  Tong Yu ,  Sungchul Kim

IPC: G06V10/75 ,  G06F16/332 ,  G06V10/774

Abstract: Systems and methods for multimodal machine learning are provided. According to one aspect, a method for multimodal machine learning includes obtaining a prompt; encoding the prompt using a multimodal encoder to obtain a prompt embedding, wherein the encoding comprises generating a plurality of multi-head attention (MHA) outputs corresponding to a plurality of different scales, respectively, and combining the plurality of MHA outputs using a multi-scale aggregator; and generating a response to the prompt based on the prompt embedding.

公开(公告)号：US11995403B2

公开(公告)日：2024-05-28

申请号：US17524282

申请日：2021-11-11

Applicant: ADOBE INC.

Inventor： Sungchul Kim ,  Subrata Mitra ,  Ruiyi Zhang ,  Rui Wang ,  Handong Zhao ,  Tong Yu

IPC: G06F40/295 ,  G06N20/00

CPC classification number: G06F40/295 ,  G06N20/00

Abstract: 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.

公开(公告)号：US11995048B2

公开(公告)日：2024-05-28

申请号：US17036453

申请日：2020-09-29

Applicant: ADOBE INC.

Inventor： Handong Zhao ,  Yikun Xian ,  Sungchul Kim ,  Tak Yeon Lee ,  Nikhil Belsare ,  Shashi Kant Rai ,  Vasanthi Holtcamp ,  Thomas Jacobs ,  Duy-Trung T Dinh ,  Caroline Jiwon Kim

IPC: G06F16/00 ,  G06F16/21 ,  G06F18/2115 ,  G06F18/214 ,  G06F18/2431 ,  G06N3/08 ,  G06V30/262

CPC classification number: G06F16/213 ,  G06F18/2115 ,  G06F18/2148 ,  G06F18/2431 ,  G06N3/08 ,  G06V30/274

Abstract: Systems and methods for lifelong schema matching are described. The systems and methods include receiving data comprising a plurality of information categories, classifying each information category according to a schema comprising a plurality of classes, wherein the classification is performed by a neural network classifier trained based on a lifelong learning technique using a plurality of exemplar training sets, wherein each of the exemplar training sets includes a plurality of examples corresponding to one of the classes, and wherein the examples are selected based on a metric indicating how well each of the examples represents the corresponding class, and adding the data to a database based on the classification, wherein the database is organized according to the schema.

公开(公告)号：US20240152769A1

公开(公告)日：2024-05-09

申请号：US18050607

申请日：2022-10-28

Applicant: ADOBE INC.

Inventor： Ryan A. Rossi ,  Kanak Mahadik ,  Mustafa Abdallah ElHosiny Abdallah ,  Sungchul Kim ,  Handong Zhao

IPC: G06N3/0985 ,  G06Q10/04

CPC classification number: G06N3/0985 ,  G06Q10/04

Abstract: 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.

公开(公告)号：US20230376828A1

公开(公告)日：2023-11-23

申请号：US17664079

申请日：2022-05-19

Applicant: ADOBE INC.

Inventor： Handong Zhao ,  Haoyu Ma ,  Zhe Lin ,  Ajinkya Gorakhnath Kale ,  Tong Yu ,  Jiuxiang Gu ,  Sunav Choudhary ,  Venkata Naveen Kumar Yadav Marri

IPC: G06N20/00 ,  G06F16/9538 ,  G06Q30/06

CPC classification number: G06N20/00 ,  G06F16/9538 ,  G06Q30/0641

Abstract: Systems and methods for product retrieval are described. One or more aspects of the systems and methods include receiving a query that includes a text description of a product associated with a brand; identifying the product based on the query by comparing the text description to a product embedding of the product, wherein the product embedding is based on a brand embedding of the brand; and displaying product information for the product in response to the query, wherein the product information includes the brand.

公开(公告)号：US20230368003A1

公开(公告)日：2023-11-16

申请号：US17740497

申请日：2022-05-10

Applicant: ADOBE INC.

Inventor： Jiuxiang Gu ,  Zihan Wang ,  Jason Wen Yong Kuen ,  Handong Zhao ,  Vlad Ion Morariu ,  Ruiyi Zhang ,  Ani Nenkova Nenkova ,  Tong Sun

IPC: G06N3/04 ,  G06F40/284

CPC classification number: G06N3/0481 ,  G06F40/284

Abstract: The technology described herein is directed to an adaptive sparse attention pattern that is learned during fine-tuning and deployed in a machine-learning model. In aspects, a row or a column in an attention matrix with an importance score for a task that is above a threshold importance score is identified. The important row or the column is included in an adaptive attention pattern used with a machine-learning model having a self-attention operation. In response to an input, a task-specific inference is generated for the input using the machine-learning model with the adaptive attention pattern.

公开(公告)号：US11782576B2

公开(公告)日：2023-10-10

申请号：US17161770

申请日：2021-01-29

Applicant: Adobe Inc.

Inventor： Camille Harris ,  Zening Qu ,  Sana Lee ,  Ryan Rossi ,  Fan Du ,  Eunyee Koh ,  Tak Yeon Lee ,  Sungchul Kim ,  Handong Zhao ,  Sumit Shekhar

IPC: G06F3/0482 ,  G06F17/15 ,  G06F3/04845

CPC classification number: G06F3/0482 ,  G06F3/04845 ,  G06F17/15

Abstract: 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.

公开(公告)号：US11645523B2

公开(公告)日：2023-05-09

申请号：US16796681

申请日：2020-02-20

Applicant: Adobe Inc.

Inventor： Yikun Xian ,  Tak Yeon Lee ,  Sungchul Kim ,  Ryan Rossi ,  Handong Zhao

IPC: G06F16/22 ,  G06F16/2457 ,  G06F16/248 ,  G06F16/901 ,  G06N3/08 ,  G06N5/02

CPC classification number: G06N3/08 ,  G06F16/221 ,  G06F16/248 ,  G06F16/24578 ,  G06F16/9024 ,  G06N5/02

Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for generating generate explanatory paths for column annotations determined using a knowledge graph and a deep representation learning model. For instance, the disclosed systems can utilize a knowledge graph to generate an explanatory path for a column label determination from a deep representation learning model. For example, the disclosed systems can identify a column and determine a label for the column using a knowledge graph (e.g., a representation of a knowledge graph) that includes encodings of columns, column features, relational edges, and candidate labels. Then, the disclosed systems can determine a set of candidate paths between the column and the determined label for the column within the knowledge graph. Moreover, the disclosed systems can generate an explanatory path by ranking and selecting paths from the set of candidate paths using a greedy ranking and/or diversified ranking approach.