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    DYNAMIC CAUSAL DISCOVERY IN IMITATION LEARNING
    43.
    发明公开
    DYNAMIC CAUSAL DISCOVERY IN IMITATION LEARNING 审中-公开

    公开(公告)号：US20240054373A1

    公开(公告)日：2024-02-15

    申请号：US18471570

    申请日：2023-09-21

    Applicant: NEC Laboratories America, Inc.

    Inventor： Wenchao Yu ,  Wei Cheng ,  Haifeng Chen ,  Yuncong Chen ,  Xuchao Zhang ,  Tianxiang Zhao

    IPC: G06N7/01 ,  G06N20/00

    CPC classification number: G06N7/01 ,  G06N20/00

    Abstract: A method for learning a self-explainable imitator by discovering causal relationships between states and actions is presented. The method includes obtaining, via an acquisition component, demonstrations of a target task from experts for training a model to generate a learned policy, training the model, via a learning component, the learning component computing actions to be taken with respect to states, generating, via a dynamic causal discovery component, dynamic causal graphs for each environment state, encoding, via a causal encoding component, discovered causal relationships by updating state variable embeddings, and outputting, via an output component, the learned policy including trajectories similar to the demonstrations from the experts.

    META IMITATION LEARNING WITH STRUCTURED SKILL DISCOVERY
    44.
    发明公开
    META IMITATION LEARNING WITH STRUCTURED SKILL DISCOVERY 审中-公开

    公开(公告)号：US20240046091A1

    公开(公告)日：2024-02-08

    申请号：US18484793

    申请日：2023-10-11

    Applicant: NEC Laboratories America, Inc.

    Inventor： Wenchao Yu ,  Wei Cheng ,  Haifeng Chen ,  Yiwei Sun

    IPC: G06N3/08 ,  G06N20/00

    CPC classification number: G06N3/08 ,  G06N20/00

    Abstract: A method for acquiring skills through imitation learning by employing a meta imitation learning framework with structured skill discovery (MILD) is presented. The method includes learning behaviors or tasks, by an agent, from demonstrations: by learning to decompose the demonstrations into segments, via a segmentation component, the segments corresponding to skills that are transferrable across different tasks, learning relationships between the skills that are transferrable across the different tasks, employing, via a graph generator, a graph neural network for learning implicit structures of the skills from the demonstrations to define structured skills, and generating policies from the structured skills to allow the agent to acquire the structured skills for application to one or more target tasks.

    INTERPRETING CONVOLUTIONAL SEQUENCE MODEL BY LEARNING LOCAL AND RESOLUTION-CONTROLLABLE PROTOTYPES
    45.
    发明公开
    INTERPRETING CONVOLUTIONAL SEQUENCE MODEL BY LEARNING LOCAL AND RESOLUTION-CONTROLLABLE PROTOTYPES 审中-公开

    公开(公告)号：US20240037397A1

    公开(公告)日：2024-02-01

    申请号：US18479385

    申请日：2023-10-02

    Applicant: NEC Laboratories America, Inc.

    Inventor： Jingchao Ni ,  Zhengzhang Chen ,  Wei Cheng ,  Bo Zong ,  Haifeng Chen

    IPC: G06N3/08 ,  G06N3/04

    CPC classification number: G06N3/08 ,  G06N3/04

    Abstract: A method interprets a convolutional sequence model. The method converts an input data sequence having input segments into output features. The method clusters the input segments into clusters using respective resolution-controllable class prototypes allocated to each of classes. Each respective class prototype includes a respective output feature subset characterizing a respective associated class. The method calculates, using the clusters, similarity scores that indicate a similarity of an output feature to a respective class prototypes responsive to distances between the output feature and the respective class prototypes. The method concatenates the similarity scores to obtain a similarity vector. The method performs a prediction and prediction support operation that provides a value of prediction and an interpretation for the value responsive to the input segments and similarity vector. The interpretation for the value of prediction is provided using only non-negative weights and lacking a weight bias in the fully connected layer.

    INTERPRETING CONVOLUTIONAL SEQUENCE MODEL BY LEARNING LOCAL AND RESOLUTION-CONTROLLABLE PROTOTYPES
    46.
    发明公开
    INTERPRETING CONVOLUTIONAL SEQUENCE MODEL BY LEARNING LOCAL AND RESOLUTION-CONTROLLABLE PROTOTYPES 审中-公开

    公开(公告)号：US20240028897A1

    公开(公告)日：2024-01-25

    申请号：US18479326

    申请日：2023-10-02

    Applicant: NEC Laboratories America, Inc.

    Inventor： Jingchao Ni ,  Zhengzhang Chen ,  Wei Cheng ,  Bo Zong ,  Haifeng Chen

    IPC: G06N3/08 ,  G06N3/04

    CPC classification number: G06N3/08 ,  G06N3/04

    Abstract: A method interprets a convolutional sequence model. The method converts an input data sequence having input segments into output features. The method clusters the input segments into clusters using respective resolution-controllable class prototypes allocated to each of classes. Each respective class prototype includes a respective output feature subset characterizing a respective associated class. The method calculates, using the clusters, similarity scores that indicate a similarity of an output feature to a respective class prototypes responsive to distances between the output feature and the respective class prototypes. The method concatenates the similarity scores to obtain a similarity vector. The method performs a prediction and prediction support operation that provides a value of prediction and an interpretation for the value responsive to the input segments and similarity vector. The interpretation for the value of prediction is provided using only non-negative weights and lacking a weight bias in the fully connected layer.

    MEDICAL EVENT PREDICTION USING A PERSONALIZED DUAL-CHANNEL COMBINER NETWORK
    47.
    发明公开
    MEDICAL EVENT PREDICTION USING A PERSONALIZED DUAL-CHANNEL COMBINER NETWORK 审中-公开

    公开(公告)号：US20240013920A1

    公开(公告)日：2024-01-11

    申请号：US18370074

    申请日：2023-09-19

    Applicant: NEC Laboratories America, Inc.

    Inventor： Jingchao Ni ,  Wei Cheng ,  Haifeng Chen ,  Takayoshi Asakura

    IPC: G16H50/20 ,  G06N3/08 ,  G16H10/60 ,  G06N3/047

    CPC classification number: G16H50/20 ,  G06N3/08 ,  G16H10/60 ,  G06N3/047

    Abstract: Systems and methods for predicting an occurrence of a medical event for a patient using a trained neural network. Historical patient data is preprocessed to generate normalized training samples, and the normalized training samples are sent to a personalized deep convolutional neural network for model pretraining and updating of model parameters. The pretrained model is stored in a remote server for utilization by a local machine for personalization during a preparation time period for a medical treatment. A normalized finetuning set is generated as output, and the model parameters are iteratively finetuned. A personal prediction score for future medical events is generated, and an operation of a medical treatment device is controlled responsive to the prediction score.

    Medical Event Prediction Using a Personalized Dual-Channel Combiner Network
    48.
    发明公开
    Medical Event Prediction Using a Personalized Dual-Channel Combiner Network 审中-公开

    公开(公告)号：US20240006069A1

    公开(公告)日：2024-01-04

    申请号：US18370049

    申请日：2023-09-19

    Applicant: NEC Laboratories America, Inc.

    Inventor： Jingchao Ni ,  Wei Cheng ,  Haifeng Chen ,  Takayoshi Asakura

    IPC: G16H50/20 ,  G06N3/08 ,  G16H10/60 ,  G06N3/047

    CPC classification number: G16H50/20 ,  G06N3/047 ,  G16H10/60 ,  G06N3/08

    Abstract: Systems and methods for predicting an occurrence of a medical event for a patient using a trained neural network. Historical patient data is preprocessed to generate normalized training samples, and the normalized training samples are sent to a personalized deep convolutional neural network for model pretraining and updating of model parameters. The pretrained model is stored in a remote server for utilization by a local machine for personalization during a preparation time period for a medical treatment. A normalized finetuning set is generated as output, and the model parameters are iteratively finetuned. A personal prediction score for future medical events is generated, and an operation of a medical treatment device is controlled responsive to the prediction score.

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