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    INFORMATION-AWARE GRAPH CONTRASTIVE LEARNING
    546.
    发明公开
    INFORMATION-AWARE GRAPH CONTRASTIVE LEARNING 审中-公开

    公开(公告)号：US20240037402A1

    公开(公告)日：2024-02-01

    申请号：US18484862

    申请日：2023-10-11

    Applicant: NEC Laboratories America, Inc.

    Inventor： Wei Cheng ,  Dongkuan Xu ,  Haifeng Chen

    IPC: G06N3/08

    CPC classification number: G06N3/08

    Abstract: A method for performing contrastive learning for graph tasks and datasets by employing an information-aware graph contrastive learning framework is presented. The method includes obtaining two semantically similar views of a graph coupled with a label for training by employing a view augmentation component, feeding the two semantically similar views into respective encoder networks to extract latent representations preserving both structure and attribute information in the two views, optimizing a contrastive loss based on a contrastive mode by maximizing feature consistency between the latent representations, training a neural network with the optimized contrastive loss, and predicting a new graph label or a new node label in the graph.

    INTERPRETING CONVOLUTIONAL SEQUENCE MODEL BY LEARNING LOCAL AND RESOLUTION-CONTROLLABLE PROTOTYPES
    549.
    发明公开
    INTERPRETING CONVOLUTIONAL SEQUENCE MODEL BY LEARNING LOCAL AND RESOLUTION-CONTROLLABLE PROTOTYPES 审中-公开

    公开(公告)号：US20240028898A1

    公开(公告)日：2024-01-25

    申请号：US18479372

    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.

    PERSONALIZED FEDERATED LEARNING VIA HETEROGENEOUS MODULAR NETWORKS
    550.
    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.

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