公开(公告)号：US20250149133A1

公开(公告)日：2025-05-08

申请号：US18922837

申请日：2024-10-22

Applicant: NEC Laboratories America, Inc.

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

IPC: G16H10/60 ,  G06N3/0455

Abstract: Systems and methods for optimizing key performance indicators (KPIs) using adversarial imitation deep learning include processing sensor data received from sensors to remove irrelevant data based on correlation to a final KPI and generating, using a policy generator network with a transformer-based architecture, an optimal sequence of actions based on the processed sensor data. A discriminator network is employed to differentiate between the generated action sequences and real-world high performance sequences employing. Final KPI results are estimated based on the generated action sequences using a performance prediction network. The generated action sequences are applied to the process to optimize the KPI in real-time.

公开(公告)号：US20240054373A1

公开(公告)日：2024-02-15

申请号：US18471570

申请日：2023-09-21

Applicant: NEC Laboratories America, Inc.

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

IPC: G06N7/01 ,  G06N20/00

CPC classification number: G06N7/01 ,  G06N20/00

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.

公开(公告)号：US20240046091A1

公开(公告)日：2024-02-08

申请号：US18484793

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：US20220058240A9

公开(公告)日：2022-02-24

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：US20210319847A1

公开(公告)日：2021-10-14

申请号：US17197166

申请日：2021-03-10

Applicant: NEC Laboratories America, Inc.

Inventor： Renqiang Min ,  Wenchao Yu ,  Hans Peter Graf ,  Igor Durdanovic

IPC: G16B15/30 ,  G16B40/00 ,  G06N3/08 ,  G06N3/04

Abstract: A method is provided for peptide-based vaccine generation. The method receives a dataset of positive and negative binding peptide sequences. The method pre-trains a set of peptide binding property predictors on the dataset to generate training data. The method trains a Wasserstein Generative Adversarial Network (WGAN) only on the positive binding peptide sequences, in which a discriminator of the WGAN is updated to distinguish generated peptide sequences from sampled positive peptide sequences from the training data, and a generator of the WGAN is updated to fool the discriminator. The method trains the WGAN only on the positive binding peptide sequences while simultaneously updating the generator to minimize a kernel Maximum Mean Discrepancy (MMD) loss between the generated peptide sequences and the sampled peptide sequences and maximize prediction accuracies of a set of pre-trained peptide binding property predictors with parameters of the set of pre-trained peptide binding property predictors being fixed.

公开(公告)号：US20210103706A1

公开(公告)日：2021-04-08

申请号：US17060850

申请日：2020-10-01

Applicant: NEC Laboratories America, Inc.

Inventor： Wenchao Yu ,  Bo Zong ,  Wei Cheng ,  Haifeng Chen ,  Xiusi Chen

IPC: G06F40/58 ,  G06F40/44 ,  G06N5/02 ,  G06N5/04 ,  G06N3/04

Abstract: Methods and systems for performing a knowledge graph task include aligning multiple knowledge graphs and performing a knowledge graph task using the aligned multiple knowledge graphs. Aligning the multiple knowledge graphs includes updating entity representations based on representations of neighboring entities within each knowledge graph, updating entity representations based on representations of entities from different knowledge graphs, and learning machine learning model parameters to align the multiple knowledge graphs, based on the updated entity representations.

公开(公告)号：US20190130212A1

公开(公告)日：2019-05-02

申请号：US16169184

申请日：2018-10-24

Applicant: NEC Laboratories America, Inc.

Inventor： Wei Cheng ,  Haifeng Chen ,  Kenji Yoshihira ,  Wenchao Yu

IPC: G06K9/62 ,  G06N3/08 ,  G06N3/04

Abstract: Methods and systems for embedding a network in a latent space include generating a representation of an input network graph in the latent space using an autoencoder model and generating a representation of a set of noise samples in the latent space using a generator model. A discriminator model discriminates between the representation of the input network graph and the representation of the set of noise samples. The autoencoder model, the generator model, and the discriminator model are jointly trained by minimizing a joint loss function that includes parameters for each model. A final representation of the input network graph is generated using the trained autoencoder model.

公开(公告)号：US20240046128A1

公开(公告)日：2024-02-08

申请号：US18471564

申请日：2023-09-21

Applicant: NEC Laboratories America, Inc.

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

IPC: G16H50/20

CPC classification number: G16H50/20

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.

公开(公告)号：US20240005163A1

公开(公告)日：2024-01-04

申请号：US18362193

申请日：2023-07-31

Applicant: NEC Laboratories America, Inc.

Inventor： Wenchao Yu ,  Haifeng Chen

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

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

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：US20230376773A1

公开(公告)日：2023-11-23

申请号：US18362125

申请日：2023-07-31

Applicant: NEC Laboratories America, Inc.

Inventor： Wenchao Yu ,  Haifeng Chen

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

CPC classification number: G06N3/084 ,  G06N5/046 ,  G06N3/045 ,  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.