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    TRAINING A NEURAL NETWORK USING AUGMENTED TRAINING DATASETS
    61.
    发明申请
    TRAINING A NEURAL NETWORK USING AUGMENTED TRAINING DATASETS 审中-公开

    公开(公告)号：US20190130218A1

    公开(公告)日：2019-05-02

    申请号：US15801297

    申请日：2017-11-01

    Applicant: salesforce.com, inc.

    Inventor： Evan Albright ,  Caiming Xiong

    IPC: G06K9/62 ,  G06N3/08 ,  G06N3/04 ,  G06K9/00 ,  G06K9/20 ,  G06T11/60

    CPC classification number: G06K9/6257 ,  G06K9/00664 ,  G06K9/2054 ,  G06K9/6262 ,  G06K9/627 ,  G06N3/04 ,  G06N3/08 ,  G06T11/60

    Abstract: A computer system generates augmented training datasets to train neural network models. The computer system receives an initial training dataset comprising images for training a neural network model, and generates an augmented training dataset by modifying images from the first training dataset. The computer system identifies a representation of a target object against a background from the initial training dataset and extracts a portion of the image displaying the target object. The computer system generates samples for including in the augmented training dataset based on the image. For example, new images may be obtained by performing transformations on the portion of the image displaying the target object and/or by overlaying the transformed portion of the image over a different background. The modified images are included in the augmented training dataset used for training the neural network model to recognize the target object.

    Dynamic Memory Network
    62.
    发明申请
    Dynamic Memory Network 审中-公开
    动态内存网络

    公开(公告)号：US20170024645A1

    公开(公告)日：2017-01-26

    申请号：US15221532

    申请日：2016-07-27

    Applicant: salesforce.com, inc.

    Inventor： Richard Socher ,  Ankit Kumar ,  Ozan Irsoy ,  Mohit Iyyer ,  Caiming Xiong ,  Stephen Merity ,  Romain Paulus

    IPC: G06N3/08 ,  G06N5/02

    CPC classification number: G06N3/08 ,  G06F16/3329 ,  G06F16/3347 ,  G06N3/0427 ,  G06N3/0445

    Abstract: A novel unified neural network framework, the dynamic memory network, is disclosed. This unified framework reduces every task in natural language processing to a question answering problem over an input sequence. Inputs and questions are used to create and connect deep memory sequences. Answers are then generated based on dynamically retrieved memories.

    Abstract translation: 公开了一种新颖的统一神经网络框架，动态存储网络。 这个统一框架将自然语言处理中的每个任务都减少到一个输入序列中的问题回答问题。 输入和问题用于创建和连接深层记忆序列。 然后基于动态检索的存储器生成答案。

    Dialogue state tracking using a global-local encoder
    63.
    发明授权
    Dialogue state tracking using a global-local encoder 有权

    公开(公告)号：US11836451B2

    公开(公告)日：2023-12-05

    申请号：US17179933

    申请日：2021-02-19

    Applicant: salesforce.com, inc.

    Inventor： Victor Zhong ,  Caiming Xiong

    IPC: G06F40/30 ,  G06N3/08 ,  G06N5/022 ,  G06N3/084 ,  G06N3/044 ,  G06N3/045

    CPC classification number: G06F40/30 ,  G06N3/044 ,  G06N3/045 ,  G06N3/08 ,  G06N3/084 ,  G06N5/022

    Abstract: A method for maintaining a dialogue state associated with a dialogue between a user and a digital system includes receiving, by a dialogue state tracker associated with the digital system, a representation of a user communication, updating, by the dialogue state tracker, the dialogue state and providing a system response based on the updated dialogue state. The dialogue state is updated by evaluating, based on the representation of the user communication, a plurality of member scores corresponding to a plurality of ontology members of an ontology set, and selecting, based on the plurality of member scores, zero or more of the plurality of ontology members to add to or remove from the dialogue state. The dialogue state tracker includes a global-local encoder that includes a global branch and a local branch, the global branch having global trained parameters that are shared among the plurality of ontology members and the local branch having local trained parameters that are determined separately for each of the plurality of ontology members.

    System and methods for training task-oriented dialogue (TOD) language models
    64.
    发明授权
    System and methods for training task-oriented dialogue (TOD) language models 有权

    公开(公告)号：US11749264B2

    公开(公告)日：2023-09-05

    申请号：US17088206

    申请日：2020-11-03

    Applicant: salesforce.com, inc.

    Inventor： Chien-Sheng Wu ,  Chu Hong Hoi ,  Richard Socher ,  Caiming Xiong

    IPC: G10L15/18 ,  G10L15/06

    CPC classification number: G10L15/1815 ,  G10L15/063 ,  G10L15/1822

    Abstract: Embodiments described herein provide methods and systems for training task-oriented dialogue (TOD) language models. In some embodiments, a TOD language model may receive a TOD dataset including a plurality of dialogues and a model input sequence may be generated from the dialogues using a first token prefixed to each user utterance and a second token prefixed to each system response of the dialogues. In some embodiments, the first token or the second token may be randomly replaced with a mask token to generate a masked training sequence and a masked language modeling (MLM) loss may be computed using the masked training sequence. In some embodiments, the TOD language model may be updated based on the MLM loss.

    Continual neural network learning via explicit structure learning
    69.
    发明授权
    Continual neural network learning via explicit structure learning 有权

    公开(公告)号：US11645509B2

    公开(公告)日：2023-05-09

    申请号：US16176419

    申请日：2018-10-31

    Applicant: salesforce.com, inc.

    Inventor： Yingbo Zhou ,  Xilai Li ,  Caiming Xiong

    IPC: G06N3/08 ,  G06N3/04

    CPC classification number: G06N3/08 ,  G06N3/04

    Abstract: Embodiments for training a neural network using sequential tasks are provided. A plurality of sequential tasks are received. For each task in the plurality of tasks a copy of the neural network that includes a plurality of layers is generated. From the copy of the neural network a task specific neural network is generated by performing an architectural search on the plurality of layers in the copy of the neural network. The architectural search identifies a plurality of candidate choices in the layers of the task specific neural network. Parameters in the task specific neural network that correspond to the plurality of candidate choices and that maximize architectural weights at each layer are identified. The parameters are retrained and merged with the neural network. The neural network trained on the plurality of sequential tasks is a trained neural network.

    Multi-hop knowledge graph reasoning with reward shaping
    70.
    发明授权
    Multi-hop knowledge graph reasoning with reward shaping 有权

    公开(公告)号：US11631009B2

    公开(公告)日：2023-04-18

    申请号：US16051309

    申请日：2018-07-31

    Applicant: salesforce.com, inc.

    Inventor： Xi Victoria Lin ,  Caiming Xiong ,  Richard Socher

    IPC: G06N20/00 ,  G06N5/04 ,  G06N3/04 ,  G06N3/08 ,  G06F16/903 ,  G06F16/901

    Abstract: Approaches for multi-hop knowledge graph reasoning with reward shaping include a system and method of training a system to search relational paths in a knowledge graph. The method includes identifying, using an reasoning module, a plurality of first outgoing links from a current node in a knowledge graph, masking, using the reasoning module, one or more links from the plurality of first outgoing links to form a plurality of second outgoing links, rewarding the reasoning module with a reward of one when a node corresponding to an observed answer is reached, and rewarding the reasoning module with a reward identified by a reward shaping network when a node not corresponding to an observed answer is reached. In some embodiments, the reward shaping network is pre-trained.

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