中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

29 records (took 0.062 seconds)

    MACHINE LEARNING RECOLLECTION AS PART OF QUESTION ANSWERING USING A CORPUS
    1.
    发明申请
    MACHINE LEARNING RECOLLECTION AS PART OF QUESTION ANSWERING USING A CORPUS 有权

    公开(公告)号：US20250053562A1

    公开(公告)日：2025-02-13

    申请号：US18533620

    申请日：2023-12-08

    Applicant: Adobe Inc.

    Inventor： Joseph D. Barrow ,  Darrell Jan Dykstra ,  Varun Manjunatha ,  Nedim Lipka

    IPC: G06F16/2452 ,  G06F16/248 ,  G06N3/0455

    Abstract: Machine learning recollection techniques are described as part of question answering using a corpus. Inputs are received identifying a search query and a corpus of search data that is to be searched based on the search query. The search query is decomposed to form a plurality of decomposed queries and retrieval search results are generated by searching the corpus of search data using one or more additional terms based on the decomposed queries. A search result is synthesized based on the retrieval search results using a text generation machine-learning model. The search result is presented for display in a user interface.

    SYSTEMS AND METHODS FOR DATA CORRECTION
    2.
    发明公开
    SYSTEMS AND METHODS FOR DATA CORRECTION 审中-公开

    公开(公告)号：US20240135165A1

    公开(公告)日：2024-04-25

    申请号：US18047335

    申请日：2022-10-18

    Applicant: ADOBE INC.

    Inventor： Varun Manjunatha ,  Sarthak Jain ,  Rajiv Bhawanji Jain ,  Ani Nenkova Nenkova ,  Christopher Alan Tensmeyer ,  Franck Dernoncourt ,  Quan Hung Tran ,  Ruchi Deshpande

    IPC: G06N3/08 ,  G06F40/295

    CPC classification number: G06N3/08 ,  G06F40/295

    Abstract: One aspect of systems and methods for data correction includes identifying a false label from among predicted labels corresponding to different parts of an input sample, wherein the predicted labels are generated by a neural network trained based on a training set comprising training samples and training labels corresponding to parts of the training samples; computing an influence of each of the training labels on the false label by approximating a change in a conditional loss for the neural network corresponding to each of the training labels; identifying a part of a training sample of the training samples and a corresponding source label from among the training labels based on the computed influence; and modifying the training set based on the identified part of the training sample and the corresponding source label to obtain a corrected training set.

    Domain Adaptation for Machine Learning Models
    4.
    发明申请
    Domain Adaptation for Machine Learning Models 有权

    公开(公告)号：US20220391768A1

    公开(公告)日：2022-12-08

    申请号：US17883811

    申请日：2022-08-09

    Applicant: Adobe Inc.

    Inventor： Kai Li ,  Christopher Alan Tensmeyer ,  Curtis Michael Wigington ,  Handong Zhao ,  Nikolaos Barmpalios ,  Tong Sun ,  Varun Manjunatha ,  Vlad Ion Morariu

    IPC: G06N20/00 ,  G06N3/04

    Abstract: Adapting a machine learning model to process data that differs from training data used to configure the model for a specified objective is described. A domain adaptation system trains the model to process new domain data that differs from a training data domain by using the model to generate a feature representation for the new domain data, which describes different content types included in the new domain data. The domain adaptation system then generates a probability distribution for each discrete region of the new domain data, which describes a likelihood of the region including different content described by the feature representation. The probability distribution is compared to ground truth information for the new domain data to determine a loss function, which is used to refine model parameters. After determining that model outputs achieve a threshold similarity to the ground truth information, the model is output as a domain-agnostic model.

    SYNTOPICAL READING FOR COLLECTION UNDERSTANDING
    8.
    发明申请
    SYNTOPICAL READING FOR COLLECTION UNDERSTANDING 有权

    公开(公告)号：US20230033114A1

    公开(公告)日：2023-02-02

    申请号：US17384136

    申请日：2021-07-23

    Applicant: ADOBE INC.

    Inventor： Joseph Barrow ,  Rajiv Bhawanji Jain ,  Nedim Lipka ,  Vlad Ion Morariu ,  Franck Dernoncourt ,  Varun Manjunatha

    IPC: G06F16/35 ,  G06F40/169 ,  G06F40/289 ,  G06N3/04

    Abstract: Systems and methods for natural language processing are described. One or more embodiments of the present disclosure identify a claim from a document, wherein the claim corresponds to a topic, create a graph comprising a plurality of nodes having a plurality of node types and a plurality of edges having a plurality of edge types, wherein one of the nodes represents the claim, and wherein each of the edges represents a relationship between a corresponding pair of the nodes, encode the claim based on the graph using a graph convolutional network (GCN) to obtain an encoded claim, classify the claim by decoding the encoded claim to obtain a stance label that indicates a stance of the claim towards the topic, and transmit information indicating a viewpoint of the document towards the topic based on the stance label.

    Self-supervised document representation learning
    10.
    发明授权
    Self-supervised document representation learning 有权

    公开(公告)号：US11886815B2

    公开(公告)日：2024-01-30

    申请号：US17333892

    申请日：2021-05-28

    Applicant: Adobe Inc.

    Inventor： Jiuxiang Gu ,  Vlad Morariu ,  Varun Manjunatha ,  Tong Sun ,  Rajiv Jain ,  Peizhao Li ,  Jason Kuen ,  Handong Zhao

    IPC: G06F40/279 ,  G06F40/205 ,  G06F16/93 ,  G06F40/30 ,  G06N3/088 ,  G06N3/045

    CPC classification number: G06F40/279 ,  G06F16/93 ,  G06F40/205 ,  G06F40/30 ,  G06N3/045 ,  G06N3/088

    Abstract: One example method involves operations for a processing device that include receiving, by a machine learning model trained to generate a search result, a search query for a text input. The machine learning model is trained by receiving pre-training data that includes multiple documents. Pre-training the machine learning model by generating, using an encoder, feature embeddings for each of the documents included in the pre-training data. The feature embeddings are generated by applying a masking function to visual and textual features in the documents. Training the machine learning model also includes generating, using the feature embeddings, output features for the documents by concatenating the feature embeddings and applying a non-linear mapping to the feature embeddings. Training the machine learning model further includes applying a linear classifier to the output features. Additionally, operations include generating, for display, a search result using the machine learning model based on the input.

Patent Agency Ranking