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    INTERPRETING CONVOLUTIONAL SEQUENCE MODEL BY LEARNING LOCAL AND RESOLUTION-CONTROLLABLE PROTOTYPES
    481.
    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
    483.
    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
    484.
    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
    485.
    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.

    Multi-scale multi-granularity spatial-temporal traffic volume prediction
    487.
    发明授权
    Multi-scale multi-granularity spatial-temporal traffic volume prediction 有权

    公开(公告)号：US11842271B2

    公开(公告)日：2023-12-12

    申请号：US17003112

    申请日：2020-08-26

    Applicant: NEC Laboratories America, Inc.

    Inventor： Yanchi Liu ,  Wei Cheng ,  Bo Zong ,  LuAn Tang ,  Haifeng Chen ,  Denghui Zhang

    IPC: G06N3/04 ,  G06N3/08 ,  G06N3/049

    CPC classification number: G06N3/08 ,  G06N3/049

    Abstract: Methods and systems for allocating network resources responsive to network traffic include modeling spatial correlations between fine spatial granularity traffic and coarse spatial granularity traffic for different sites and regions to determine spatial feature vectors for one or more sites in a network. Temporal correlations at a fine spatial granularity are modeled across multiple temporal scales, based on the spatial feature vectors. Temporal correlations at a coarse spatial granularity are modeled across multiple temporal scales, based on the spatial feature vectors. A traffic flow prediction is determined for the one or more sites in the network, based on the temporal correlations at the fine spatial granularity and the temporal correlations at the coarse spatial granularity. Network resources are provisioned at the one or more sites in accordance with the traffic flow prediction.

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