公开(公告)号：US11783189B2

公开(公告)日：2023-10-10

申请号：US16998228

申请日：2020-08-20

Applicant: NEC Laboratories America, Inc.

Inventor： Wenchao Yu ,  Haifeng Chen

IPC: G06N3/08 ,  G06N3/084 ,  G16H50/30 ,  G06N20/20 ,  G06N5/046 ,  G06N3/045

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

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.

公开(公告)号：US20210248461A1

公开(公告)日：2021-08-12

申请号：US17153160

申请日：2021-01-20

Applicant: NEC Laboratories America, Inc.

Inventor： Wei Cheng ,  Haifeng Chen ,  Wenchao Yu

IPC: G06N3/08 ,  G06N3/04

Abstract: A method for employing a graph enhanced attention network for explainable point-of-interest (POI) recommendation (GEAPR) is presented. The method includes interpreting POI prediction in an end-to-end fashion by adopting an adaptive neural network, learning user representations by aggregating a plurality of factors, the plurality of factors including structural context, neighbor impact, user attributes, and geolocation influence, and quantifying each of the plurality of factors by numeric values as feature salience indicators.

公开(公告)号：US20210065009A1

公开(公告)日：2021-03-04

申请号：US16998228

申请日：2020-08-20

Applicant: NEC Laboratories America, Inc.

Inventor： Wenchao Yu ,  Haifeng Chen

IPC: G06N3/08 ,  G06N3/04 ,  G06N5/04 ,  G06N20/20 ,  G16H50/30

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.

公开(公告)号：US20210064998A1

公开(公告)日：2021-03-04

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 ,  G06N3/04

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：US20210064689A1

公开(公告)日：2021-03-04

申请号：US16987734

申请日：2020-08-07

Applicant: NEC Laboratories America, Inc.

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

IPC: G06F17/18 ,  G06K9/62 ,  G06N5/04 ,  G06N3/04

Abstract: A method for unsupervised multivariate time series trend detection for group behavior analysis is presented. The method includes collecting multi-variate time series data from a plurality of sensors, learning piecewise linear trends jointly for all of the multi-variate time series data, dividing the multi-variate time series data into a plurality of time segments, counting a number of up/down trends in each of the plurality of time segments, for a training phase, employing a cumulative sum (CUSUM), and, for a testing phase, monitoring the CUSUM for trend changes.

公开(公告)号：US20200092316A1

公开(公告)日：2020-03-19

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 ,  G06K9/62 ,  G06F16/901

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：US20250104824A1

公开(公告)日：2025-03-27

申请号：US18828410

申请日：2024-09-09

Applicant: NEC Laboratories America, Inc.

Inventor： Wei Cheng ,  Wenchao Yu ,  Yanchi Liu ,  Xujiang Zhao ,  Haifeng Chen ,  Yijia Xiao

IPC: G16H20/00 ,  G06F21/62 ,  G06F40/284 ,  G16H10/60

Abstract: Methods and systems include annotating a set of training data to indicate tokens that are sensitive. Instructions are generated based on the training data, including original token sequences and respective substituted token sequences. A language model is fine-tuned using the instructions with a penalty-based loss function to generate a privacy-protected language model.

公开(公告)号：US20240104393A1

公开(公告)日：2024-03-28

申请号：US18466333

申请日：2023-09-13

Applicant: NEC Laboratories America, Inc.

Inventor： Wei Cheng ,  Wenchao Yu ,  Haifeng Chen ,  Yue Wu

IPC: G06N3/098

CPC classification number: G06N3/098

Abstract: Systems and methods for personalized federated learning. The method may include receiving at a central server local models from a plurality of clients, and aggregating a heterogeneous data distribution extracted from the local models. The method can further include processing the data distribution as a linear mixture of joint distributions to provide a global learning model, and transmitting the global learning model to the clients. The global learning model is used to update the local model.

公开(公告)号：US20240037400A1

公开(公告)日：2024-02-01

申请号：US18484805

申请日：2023-10-11

Applicant: NEC Laboratories America, Inc.

Inventor： Wenchao Yu ,  Wei Cheng ,  Haifeng Chen ,  Yiwei Sun

IPC: G06N3/08 ,  G06N20/00

CPC classification number: G06N3/08 ,  G06N20/00

Abstract: A method for acquiring skills through imitation learning by employing a meta imitation learning framework with structured skill discovery (MILD) is presented. The method includes learning behaviors or tasks, by an agent, from demonstrations: by learning to decompose the demonstrations into segments, via a segmentation component, the segments corresponding to skills that are transferrable across different tasks, learning relationships between the skills that are transferrable across the different tasks, employing, via a graph generator, a graph neural network for learning implicit structures of the skills from the demonstrations to define structured skills, and generating policies from the structured skills to allow the agent to acquire the structured skills for application to one or more target tasks.

公开(公告)号：US20230080424A1

公开(公告)日：2023-03-16

申请号：US17877081

申请日：2022-07-29

Applicant: NEC Laboratories America, Inc.

Inventor： Wenchao Yu ,  Wei Cheng ,  Haifeng Chen ,  Yuncong Chen ,  Xuchao Zhang ,  Tianxiang Zhao

IPC: G06N7/00

Abstract: A method for learning a self-explainable imitator by discovering causal relationships between states and actions is presented. The method includes obtaining, via an acquisition component, demonstrations of a target task from experts for training a model to generate a learned policy, training the model, via a learning component, the learning component computing actions to be taken with respect to states, generating, via a dynamic causal discovery component, dynamic causal graphs for each environment state, encoding, via a causal encoding component, discovered causal relationships by updating state variable embeddings, and outputting, via an output component, the learned policy including trajectories similar to the demonstrations from the experts.