公开(公告)号：US11610114B2

公开(公告)日：2023-03-21

申请号：US16675596

申请日：2019-11-06

Applicant: NEC Laboratories America, Inc.

Inventor： Bo Zong ,  Jingchao Ni ,  Haifeng Chen ,  Cheng Zheng

IPC: G06N3/08 ,  G06N3/04

Abstract: A method for employing a supervised graph sparsification (SGS) network to use feedback from subsequent graph learning tasks to guide graph sparsification is presented. The method includes, in a training phase, generating sparsified subgraphs by edge sampling from input training graphs following a learned distribution, feeding the sparsified subgraphs to a prediction/classification component, collecting a predication/classification error, and updating parameters of the learned distribution based on a gradient derived from the predication/classification error. The method further includes, in a testing phase, generating sparsified subgraphs by edge sampling from input testing graphs following the learned distribution, feeding the sparsified subgraphs to the prediction/classification component, and outputting prediction/classification results to a visualization device.

公开(公告)号：US11496493B2

公开(公告)日：2022-11-08

申请号：US16565746

申请日：2019-09-10

Applicant: NEC Laboratories America, Inc.

Inventor： LuAn Tang ,  Jingchao Ni ,  Wei Cheng ,  Haifeng Chen ,  Dongjin Song ,  Bo Zong ,  Wenchao Yu

IPC: H04L29/06 ,  H04L9/40 ,  G06F16/901 ,  G06K9/62

Abstract: Systems and methods for implementing dynamic graph analysis (DGA) to detect anomalous network traffic are provided. The method includes processing communications and profile data associated with multiple devices to determine dynamic graphs. The method includes generating features to model temporal behaviors of network traffic generated by the multiple devices based on the dynamic graphs. The method also includes formulating a list of prediction results for sources of the anomalous network traffic from the multiple devices based on the temporal behaviors.

公开(公告)号：US20220164600A1

公开(公告)日：2022-05-26

申请号：US17528394

申请日：2021-11-17

Applicant: NEC Laboratories America, Inc.

Inventor： Wei Cheng ,  Haifeng Chen ,  Jingchao Ni ,  Dongsheng Luo

IPC: G06K9/62 ,  G06N3/08

Abstract: Systems and methods for augmenting data sets is provided. The systems and methods includes feeding an original document into a data augmentation generator to produce one or more augmented documents; calculating a contrastive loss between the original document and the one or more augmented documents; and using the original document and the one or more augmented documents to train a neural network.

公开(公告)号：US20210232919A1

公开(公告)日：2021-07-29

申请号：US17158483

申请日：2021-01-26

Applicant: NEC Laboratories America, Inc.

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

IPC: G06N3/08 ,  G06F9/48

Abstract: Methods and systems for training a neural network model include training a modular neural network model, which has a shared encoder and one or more task-specific decoders, including training one or more policy networks that control connections between the shared encoder and the one or more task-specific decoders in accordance with multiple tasks. A multitask neural network model is trained for the multiple tasks, with an output of the modular neural network model and the multitask neural network model being combined to form a final output.

公开(公告)号：US20200092315A1

公开(公告)日：2020-03-19

申请号：US16562805

申请日：2019-09-06

Applicant: NEC Laboratories America, Inc.

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

IPC: H04L29/06 ,  G06N3/08

Abstract: Systems and methods for implementing sequence data based temporal behavior analysis (SDTBA) to extract features for characterizing temporal behavior of network traffic are provided. The method includes extracting communication and profile data associated with one or more devices to determine sequences of data associated with the devices. The method includes generating temporal features to model anomalous network traffic. The method also includes inputting, into an anomaly detection process for anomalous network traffic, the temporal features and the sequences of data associated with the devices and formulating a list of prediction results of anomalous network traffic associated with the devices.

公开(公告)号：US20180067831A1

公开(公告)日：2018-03-08

申请号：US15661625

申请日：2017-07-27

Applicant: NEC Laboratories America, Inc.

Inventor： Wei Cheng ,  Haifeng Chen ,  Guofei Jiang ,  Jingchao Ni

IPC: G06F11/22 ,  G06N99/00

CPC classification number: G06F11/2257 ,  G06F17/504 ,  G06N5/048 ,  G06N20/00

Abstract: A computer-implemented method for diagnosing system faults by fine-grained causal anomaly inference is presented. The computer-implemented method includes identifying functional modules impacted by causal anomalies and backtracking causal anomalies in impaired functional modules by a low-rank network diffusion model. An invariant network and a broken network are inputted into the system, the invariant network and the broken network being jointly clustered to learn a degree of broken severities of different clusters as a result of fault propagations.

公开(公告)号：US20240070232A1

公开(公告)日：2024-02-29

申请号：US18452664

申请日：2023-08-21

Applicant: NEC Laboratories America, Inc.

Inventor： Wei Cheng ,  Jingchao Ni ,  Liang Tong ,  Haifeng Chen ,  Yizhou Zhang

IPC: G06F18/2413 ,  G06F18/2415 ,  H04B10/69

CPC classification number: G06F18/24133 ,  G06F18/2415 ,  H04B10/697

Abstract: Methods and systems for training a model include determining class prototypes of time series samples from a training dataset. A task corresponding to the time series samples is encoded using the class prototypes and a task-level configuration. A likelihood value is determined based on outputs of a time series density model, a task-class distance from a task embedding model, and a task density model. Parameters of the time series density model, the task embedding model, and the task density model are adjusted responsive to the likelihood value.

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.

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.

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.