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    System and methods for training task-oriented dialogue (TOD) language models
    1.
    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.

    System and method for learning with noisy labels as semi-supervised learning
    5.
    发明授权
    System and method for learning with noisy labels as semi-supervised learning 有权

    公开(公告)号：US11599792B2

    公开(公告)日：2023-03-07

    申请号：US16688104

    申请日：2019-11-19

    Applicant: salesforce.com, inc.

    Inventor： Junnan Li ,  Chu Hong Hoi

    IPC: G06N3/08 ,  G06N3/04

    Abstract: A method provides learning with noisy labels. The method includes generating a first network of a machine learning model with a first set of parameter initial values, and generating a second network of the machine learning model with a second set of parameter initial values. First clean probabilities for samples in a training dataset are generated using the second network. A first labeled dataset and a first unlabeled dataset are generated from the training dataset based on the first clean probabilities. The first network is trained based on the first labeled dataset and first unlabeled dataset to update parameters of the first network.

    Noise-resistant object detection with noisy annotations
    7.
    发明授权
    Noise-resistant object detection with noisy annotations 有权

    公开(公告)号：US11334766B2

    公开(公告)日：2022-05-17

    申请号：US16778339

    申请日：2020-01-31

    Applicant: salesforce.com, inc.

    Inventor： Junnan Li ,  Chu Hong Hoi

    IPC: G06K9/62 ,  G06N3/04 ,  G06N3/08

    Abstract: Systems and methods are provided for training object detectors of a neural network model with a mixture of label noise and bounding box noise. According to some embodiments, a learning framework is provided which jointly optimizes object labels, bounding box coordinates, and model parameters by performing alternating noise correction and model training. In some embodiments, to disentangle label noise and bounding box noise, a two-step noise correction method is employed. In some examples, the first step performs class-agnostic bounding box correction by minimizing classifier discrepancy and maximizing region objectness. In some examples, the second step uses dual detection heads for label correction and class-specific bounding box refinement.

    SYSTEMS AND METHODS FOR PARTIALLY SUPERVISED LEARNING WITH MOMENTUM PROTOTYPES
    8.
    发明申请
    SYSTEMS AND METHODS FOR PARTIALLY SUPERVISED LEARNING WITH MOMENTUM PROTOTYPES 有权

    公开(公告)号：US20220067506A1

    公开(公告)日：2022-03-03

    申请号：US17005763

    申请日：2020-08-28

    Applicant: salesforce.com, inc.

    Inventor： Junnan Li ,  Chu Hong Hoi

    IPC: G06N3/08 ,  G06K9/62

    Abstract: A learning mechanism with partially-labeled web images is provided while correcting the noise labels during the learning. Specifically, the mechanism employs a momentum prototype that represents common characteristics of a specific class. One training objective is to minimize the difference between the normalized embedding of a training image sample and the momentum prototype of the corresponding class. Meanwhile, during the training process, the momentum prototype is used to generate a pseudo label for the training image sample, which can then be used to identify and remove out of distribution (OOD) samples to correct the noisy labels from the original partially-labeled training images. The momentum prototype for each class is in turn constantly updated based on the embeddings of new training samples and their pseudo labels.

    SYSTEMS AND METHODS FOR UNSUPERVISED ANOMALY DETECTION
    9.
    发明公开
    SYSTEMS AND METHODS FOR UNSUPERVISED ANOMALY DETECTION 审中-公开

    公开(公告)号：US20230244925A1

    公开(公告)日：2023-08-03

    申请号：US17589595

    申请日：2022-01-31

    Applicant: salesforce.com, inc.

    Inventor： Wenzhuo Yang ,  Chu Hong Hoi ,  Kun Zhang

    IPC: G06N3/08 ,  G06K9/62

    CPC classification number: G06N3/08 ,  G06K9/6284 ,  G06K9/6262

    Abstract: Embodiments described herein provide a system and method for unsupervised anomaly detection. The system receives, via a communication interface, a dataset of instances that include anomalies. The system determines, via an inlier model, a set of noisy labels. The system trains a causality-based label-noise model based at least in part on the set of noisy labels and the set of high-confidence instances. The system determines an estimated proportion of anomalies in the dataset of instances. The system retrains the inlier model based on the estimated inlier samples. The system iteratively retrains the inlier model and the trained causality-based label-noise model based on the output from the corresponding retrained models not converging within the convergence threshold. The system extracts the anomaly detection model from the iteratively trained causality-based label-noise model.

    Systems and methods for explicit memory tracker with coarse-to-fine reasoning in conversational machine reading
    10.
    发明授权
    Systems and methods for explicit memory tracker with coarse-to-fine reasoning in conversational machine reading 有权

    公开(公告)号：US11640505B2

    公开(公告)日：2023-05-02

    申请号：US16863999

    申请日：2020-04-30

    Applicant: salesforce.com, inc.

    Inventor： Yifan Gao ,  Chu Hong Hoi ,  Shafiq Rayhan Joty ,  Chien-Sheng Wu

    IPC: G06F40/289 ,  G06F16/332

    Abstract: Embodiments described herein provide systems and methods for an Explicit Memory Tracker (EMT) that tracks each rule sentence to perform decision making and to generate follow-up clarifying questions. Specifically, the EMT first segments the regulation text into several rule sentences and allocates the segmented rule sentences into memory modules, and then feeds information regarding the user scenario and dialogue history into the EMT sequentially to update each memory module separately. At each dialogue turn, the EMT makes a decision among based on current memory status of the memory modules whether further clarification is needed to come up with an answer to a user question. The EMT determines that further clarification is needed by identifying an underspecified rule sentence span by modulating token-level span distributions with sentence-level selection scores. The EMT extracts the underspecified rule sentence span and rephrases the underspecified rule sentence span to generate a follow-up question.

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