公开(公告)号：US20240212865A1

公开(公告)日：2024-06-27

申请号：US18539506

申请日：2023-12-14

Applicant: NEC Laboratories America, Inc.

Inventor： Wenchao Yu ,  Wei Cheng ,  Haifeng Chen

IPC: G16H50/70 ,  G16H10/60 ,  G16H20/00 ,  G16H50/20

CPC classification number: G16H50/70 ,  G16H10/60 ,  G16H20/00 ,  G16H50/20

Abstract: Methods and systems for training a healthcare treatment machine learning model include segmenting a patient trajectory, which includes a sequence of patient states and treatment actions. A machine learning model is trained based on segments of the patient trajectory, including a prototype layer that learns prototype vectors representing respective classes of trajectory segments and an imitation learning layer that learns a policy to select a treatment action based on an input state and a skill embedding.

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.

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.

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.

公开(公告)号：US20230394309A1

公开(公告)日：2023-12-07

申请号：US18451880

申请日：2023-08-18

Applicant: NEC Laboratories America, Inc.

Inventor： Wei Cheng ,  Haifeng Chen ,  Jingchao Ni ,  Dongkuan Xu ,  Wenchao Yu

IPC: G06N3/08 ,  G06F17/18 ,  G06N5/04 ,  G06F18/214 ,  G06N3/044 ,  G06N3/045

CPC classification number: G06N3/08 ,  G06F17/18 ,  G06N5/04 ,  G06F18/214 ,  G06N3/044 ,  G06N3/045

Abstract: A method for executing a multi-task deep learning model for learning trends in multivariate time series is presented. The method includes collecting multi-variate time series data from a plurality of sensors, jointly learning both local and global contextual features for predicting a trend of the multivariate time series by employing a tensorized long short-term memory (LSTM) with adaptive shared memory (TLASM) to learn historical dependency of historical trends, and employing a multi-task one-dimensional convolutional neural network (1dCNN) to extract salient features from local raw time series data to model a short-term dependency between local time series data and subsequent trends.

公开(公告)号：US11783181B2

公开(公告)日：2023-10-10

申请号：US16987789

申请日：2020-08-07

Applicant: NEC Laboratories America, Inc.

Inventor： Wei Cheng ,  Haifeng Chen ,  Jingchao Ni ,  Dongkuan Xu ,  Wenchao Yu

IPC: G06N3/08 ,  G06F17/18 ,  G06N5/04 ,  G06F18/214 ,  G06N3/044 ,  G06N3/045

CPC classification number: G06N3/08 ,  G06F17/18 ,  G06F18/214 ,  G06N3/044 ,  G06N3/045 ,  G06N5/04

Abstract: A method for executing a multi-task deep learning model for learning trends in multivariate time series is presented. The method includes collecting multi-variate time series data from a plurality of sensors, jointly learning both local and global contextual features for predicting a trend of the multivariate time series by employing a tensorized long short-term memory (LSTM) with adaptive shared memory (TLASM) to learn historical dependency of historical trends, and employing a multi-task one-dimensional convolutional neural network (1dCNN) to extract salient features from local raw time series data to model a short-term dependency between local time series data and subsequent trends.

公开(公告)号：US20230252302A1

公开(公告)日：2023-08-10

申请号：US18152238

申请日：2023-01-10

Applicant: NEC Laboratories America, Inc.

Inventor： Liang Tong ,  Takehiko Mizoguchi ,  Zhengzhang Chen ,  Wei Cheng ,  Haifeng Chen ,  Nauman Ahad

IPC: G06N3/0895 ,  G06N3/0442

CPC classification number: G06N3/0895 ,  G06N3/0442

Abstract: A computer-implemented method for ordinal prediction is provided. The method includes encoding time series data with a temporal encoder to obtain latent space representations. The method includes optimizing the temporal encoder using semi-supervised learning to distinguish different classes in the labeled space using labeled data, and augment the latent space representations using unlabeled training data, to obtain semi-supervised representations. The method further includes discarding a linear layer after the temporal encoder and fixing the temporal encoder. The method also includes training k−1 binary classifiers on top of the semi-supervised representations to obtain k−1 binary predictions. The method additionally includes identifying and correcting inconsistent ones of the k−1 binary predictions by matching the inconsistent ones to consistent ones of the k−1 binary predictions. The method further includes aggregating the k−1 binary predictions to obtain an ordinal prediction.

公开(公告)号：US20230236927A1

公开(公告)日：2023-07-27

申请号：US18152546

申请日：2023-01-10

Applicant: NEC Laboratories America, Inc. ,  NEC Corporation

Inventor： LuAn Tang ,  Haifeng Chen ,  Yuncong Chen ,  Wei Cheng ,  Zhengzhang Chen ,  Yuji Kobayashi

IPC: G06F11/07 ,  G06N3/0455

CPC classification number: G06F11/0793 ,  G06F11/0721 ,  G06N3/0455

Abstract: Methods and systems for anomaly detection include determining whether a system is in a stable state or a dynamic state based on input data from one or more sensors in the system, using reconstruction errors from a respective stable model and dynamic model. It is determined that the input data represents anomalous operation of the system, responsive to a determination that the system is in a stable state, using the reconstruction errors. A corrective operation is performed on the system responsive to a determination that the input data represents anomalous operation of the system.

公开(公告)号：US20230094623A1

公开(公告)日：2023-03-30

申请号：US17950203

申请日：2022-09-22

Applicant: NEC Laboratories America, Inc.

Inventor： Jingchao Ni ,  Wei Cheng ,  Haifeng Chen

IPC: G06N3/08 ,  G06N7/00

Abstract: A computer-implemented method for model building is provided. The method includes receiving a training set of medical records and model hyperparameters. The method further includes initializing an encoder as a Dual-Channel Combiner Network (DCNN) and initialize distribution related parameters. The method also includes performing, by a hardware processor, a forward computation to (1) the DCNN to obtain the embeddings of the medical records, and (2) the distribution related parameters to obtain class probabilities. The method additionally includes checking by a convergence evaluator if the iterative optimization has converged. The method further includes performing model personalization responsive to model convergence by encoding the support data of a new patient and using the embeddings and event subtype labels to train a personalized classifier.

公开(公告)号：US11606393B2

公开(公告)日：2023-03-14

申请号：US17004547

申请日：2020-08-27

Applicant: NEC Laboratories America, Inc.

Inventor： Jingchao Ni ,  Haifeng Chen ,  Bo Zong ,  LuAn Tang ,  Wei Cheng

IPC: H04L9/40 ,  G06N3/049 ,  H04L41/16 ,  G06N3/08

Abstract: Methods and systems for detecting and responding to anomalous nodes in a network include inferring temporal factors, using a computer-implemented neural network, that represent changes in a network graph across time steps, with a temporal factor for each time step depending on a temporal factor for a previous time step. An invariant factor is inferred that represents information about the network graph that does not change across the time steps. The temporal factors and the invariant factor are combined into a combined temporal-invariant representation. It is determined that an unlabeled node is anomalous, based on the combined temporal-invariant representation. A security action is performed responsive to the determination that unlabeled node is anomalous.