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    ADVERSARIAL IMITATION LEARNING MODEL
    41.
    发明申请
    ADVERSARIAL IMITATION LEARNING MODEL 有权

    公开(公告)号：US20250148292A1

    公开(公告)日：2025-05-08

    申请号：US18620125

    申请日：2024-03-28

    Applicant: NEC Laboratories America, Inc.

    Inventor： LuAn Tang ,  Haoyu Wang ,  Haifeng Chen ,  Wenchao Yu ,  Zhengzhang Chen

    IPC: G06N3/094 ,  G06N3/0455

    Abstract: Systems and methods train a transformer-based policy network and Generative Adversarial Network (GAN) by initializing a transformer-based policy network to model action sequences by encoding temporal dependencies within sensor data. Multi-head self-attention mechanisms process sequential sensor inputs by being pre-trained on a labeled dataset having sensor data from known low-risk action sequences. A generator within the GAN is trained to produce generated action sequences, which mimic behavior of low-risk action sequences. A discriminator within the GAN is concurrently trained to differentiate between action sequences derived from the labeled dataset and synthetic action sequences produced by the generator. A feedback loop is employed to adjust parameters to produce sequences indistinguishable from real low-risk action sequences. Risk scores are generated and low-risk action sequences are identified upon reaching a predetermined threshold for accuracy in distinguishing between real and synthetic action sequences.

    FEDERATED IMITATION LEARNING FOR MEDICAL DECISION MAKING
    43.
    发明申请
    FEDERATED IMITATION LEARNING FOR MEDICAL DECISION MAKING 有权

    公开(公告)号：US20240371521A1

    公开(公告)日：2024-11-07

    申请号：US18649072

    申请日：2024-04-29

    Applicant: NEC Laboratories America, Inc.

    Inventor： Wenchao Yu ,  Haifeng Chen ,  Wei Cheng

    IPC: G16H50/20 ,  G06N3/098

    Abstract: Methods and systems for skill prediction include aggregating locally trained parameters from client systems to generate updated global parameters. Parameterized vectors from the client systems are clustered into prototype clusters. A centroid of each prototype cluster is determined and the parameterized vectors from the client systems are matched to centroids of the prototype clusters to identify sets of updated local prototype vectors. The updated global parameters and the updated local prototype vectors are distributed to the client systems.

    PERSONALIZED FEDERATED LEARNING VIA HETEROGENEOUS MODULAR NETWORKS
    46.
    发明公开
    PERSONALIZED FEDERATED LEARNING VIA HETEROGENEOUS MODULAR NETWORKS 审中-公开

    公开(公告)号：US20230394323A1

    公开(公告)日：2023-12-07

    申请号：US18311984

    申请日：2023-05-04

    Applicant: NEC Laboratories America, Inc.

    Inventor： Wei Cheng ,  Wenchao Yu ,  Xuchao Zhang ,  Haifeng Chen

    IPC: H04L41/16 ,  H04L41/142

    CPC classification number: H04L41/16 ,  H04L41/142

    Abstract: A computer-implemented method for personalizing heterogeneous clients is provided. The method includes initializing a federated modular network including a plurality of clients communicating with a server, maintaining, within the server, a heterogenous module pool having sub-blocks and a routing hypernetwork, partitioning the plurality of clients by modeling a joint distribution of each client into clusters, enabling each client to make a decision in each update to assemble a personalized model by selecting a combination of sub-blocks from the heterogenous module pool, and generating, by the routing hypernetwork, the decision for each client.

    Adversarial Cooperative Imitation Learning for Dynamic Treatment
    48.
    发明公开
    Adversarial Cooperative Imitation Learning for Dynamic Treatment 审中-公开

    公开(公告)号：US20230376774A1

    公开(公告)日：2023-11-23

    申请号：US18362166

    申请日：2023-07-31

    Applicant: NEC Laboratories America, Inc.

    Inventor： Wenchao Yu ,  Hiafeng Chen

    IPC: G06N3/084 ,  G16H50/30 ,  G06N20/20 ,  G06N5/046 ,  G06N3/045

    CPC classification number: G06N3/084 ,  G16H50/30 ,  G06N20/20 ,  G06N5/046 ,  G06N3/045

    Abstract: Methods and systems for responding to changing conditions include training a model, using a processor, using trajectories that resulted in a positive outcome and trajectories that resulted in a negative outcome. Training is performed using an adversarial discriminator to train the model to generate trajectories that are similar to historical trajectories that resulted in a positive outcome, and using a cooperative discriminator to train the model to generate trajectories that are dissimilar to historical trajectories that resulted in a negative outcome. A dynamic response regime is generated using the trained model and environment information. A response to changing environment conditions is performed in accordance with the dynamic response regime.

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