公开(公告)号：US11544530B2

公开(公告)日：2023-01-03

申请号：US16662754

申请日：2019-10-24

Applicant: NEC Laboratories America, Inc.

Inventor： Wei Cheng ,  Wenchao Yu ,  Haifeng Chen

IPC: G06N3/04 ,  G06K9/62 ,  G06N20/00

Abstract: Methods and systems for determining a network embedding include training a network embedding model using training data that includes topology information for networks and attribute information relating to vertices of the networks. An embedded representation is generated using the trained network embedding model to represent an input network, with associated attribute information, in a network topology space. A machine learning task is performed using the embedded representation as input to a machine learning model.

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：US20210374612A1

公开(公告)日：2021-12-02

申请号：US17323475

申请日：2021-05-18

Applicant: NEC Laboratories America, Inc.

Inventor： Wenchao Yu ,  Haifeng Chen ,  Wei Cheng

IPC: G06N20/00

Abstract: A method for learning prototypical options for interpretable imitation learning is presented. The method includes initializing options by bottleneck state discovery, each of the options presented by an instance of trajectories generated by experts, applying segmentation embedding learning to extract features to represent current states in segmentations by dividing the trajectories into a set of segmentations, learning prototypical options for each segment of the set of segmentations to mimic expert policies by minimizing loss of a policy and projecting prototypes to the current states, training option policy with imitation learning techniques to learn a conditional policy, generating interpretable policies by comparing the current states in the segmentations to one or more prototypical option embeddings, and taking an action based on the interpretable policies generated.

公开(公告)号：US20210248465A1

公开(公告)日：2021-08-12

申请号：US17167890

申请日：2021-02-04

Applicant: NEC Laboratories America, Inc.

Inventor： Wenchao Yu ,  Haifeng Chen ,  Wei Cheng

IPC: G06N3/08 ,  G06N3/04

Abstract: A computer-implemented method is provided for hierarchical multi-agent imitation learning. The method includes learning sub-policies for sub-tasks of a hierarchical multi-agent imitation learning task by imitating expert trajectories of expert demonstrations of the subtasks with guidance from a high-level policy corresponding to the hierarchical multi-agent imitation learning task. The method further includes collecting feedback from the sub-policies relating to updating the high-level-policy with a new observation. The method also includes updating the high-level policy with the new observation responsive to the feedback from the sub-policies. The high-level policy is configured as a contextual multi-arm bandit that sequentially selects k best sub-policies at each of a plurality of time steps based on contextual information derived from the expert demonstrations

公开(公告)号：US20210216887A1

公开(公告)日：2021-07-15

申请号：US17147035

申请日：2021-01-12

Applicant: NEC Laboratories America, Inc.

Inventor： Wenchao Yu ,  Wei Cheng ,  Haifeng Chen

IPC: G06N5/02 ,  G06K9/62 ,  G06N7/00 ,  G06N3/08

Abstract: A computer-implemented method is provided for cross-lingual knowledge graph alignment. The method includes formulating a credible aligned entity pair selection problem for cross-lingual knowledge graph alignment as a Markov decision problem having a state space, an action space, a state transition probability and a reward function. The method further includes calculating a reward for a language entity selection policy responsive to the reward function. The method also includes performing credible aligned entity selection by optimizing task-specific rewards from an alignment-oriented entity representation learning phrase. The method additionally includes providing selected entity pairs as augmented alignments to the representation learning phase.

公开(公告)号：US20250124279A1

公开(公告)日：2025-04-17

申请号：US18889610

申请日：2024-09-19

Applicant: NEC Laboratories America, Inc.

Inventor： Yuncong Chen ,  Wenchao Yu ,  Wei Cheng ,  Yanchi Liu ,  Haifeng Chen ,  Zhengzhang Chen ,  LuAn Tang ,  Liri Fang

IPC: G06N3/08 ,  G06N3/0455

Abstract: Systems and methods for training a time-series-language (TSLa) model adapted for domain-specific tasks. An encoder-decoder neural network can be trained to tokenize time-series data to obtain a discrete-to-language embedding space. The TSLa model can learn a linear mapping function by concatenating token embeddings from the discrete-to-language embedding space with positional encoding to obtain mixed-modality token sequences. Token augmentation can transform the tokens from the mixed-modality token sequences with to obtain augmented tokens. The augmented tokens can train the TSLa model using a computed token likelihood to predict next tokens for the mixed-modality token sequences to obtain a trained TSLa model. A domain-specific dataset can fine-tune the trained TSLa model to adapt the trained TSLa model to perform a domain-specific task.

公开(公告)号：US20250061353A1

公开(公告)日：2025-02-20

申请号：US18806025

申请日：2024-08-15

Applicant: NEC Laboratories America, Inc.

Inventor： Wenchao Yu ,  Wei Cheng ,  Haifeng Chen ,  Geon Lee

IPC: G06N5/022 ,  G06N20/00

Abstract: Systems and methods for time-series forecasting via multi-modal augmentation and fusion. Time-series data and modality data can be decomposed into seasonal and trend representations with trend-seasonal decomposition. Using an encoder transformer model, time-series data embeddings and modality data embeddings can be concatenated from the seasonal representations and the trend representations to obtain crossed representations. Using the encoder transformer model, the modality data embeddings and the time-series data embeddings can be processed separately to obtain singular representations. The crossed representations and the singular representations can be augmented through joint trend-seasonal decomposition to obtain augmented seasonal data and augmented trend data. Using a decoder, augmented seasonal data and augmented trend data can be fused to obtain fused augmented data. Corrective action can be performed to correct predicted future events using a system with a prediction model trained with the fused augmented data.

公开(公告)号：US20240266049A1

公开(公告)日：2024-08-08

申请号：US18425715

申请日：2024-01-29

Applicant: NEC Laboratories America, Inc.

Inventor： Wenchao Yu ,  Haifeng Chen ,  Wei Cheng

IPC: G16H50/20 ,  G16H40/20

CPC classification number: G16H50/20 ,  G16H40/20

Abstract: Methods and systems for training a healthcare treatment machine learning model include aggregating local weights from a set of clients to update a set of global weights for an imitation-based skill learning model. A set of local prototype vectors are clustered from the plurality of clients to generate clusters. Representative vectors are selected for the clusters as a set of global prototypes. Client-specific prototype vectors are determined for the clients based on the representative vectors. The updated set of global weights and the client-specific prototype vectors are distributed to the clients.

公开(公告)号：US20240062070A1

公开(公告)日：2024-02-22

申请号：US18450799

申请日：2023-08-16

Applicant: NEC Laboratories America, Inc.

Inventor： Wenchao Yu ,  Haifeng Chen ,  Tianxiang Zhao

IPC: G06N3/092 ,  G06N3/045

CPC classification number: G06N3/092 ,  G06N3/045

Abstract: Methods and systems for training a model include performing skill discovery, using a set of demonstrations that includes known-good demonstrations and noisy demonstrations, to generate a set of skills. A unidirectional skill embedding model is trained in a first training while parameters of a skill matching model and low-level policies that relate skills to actions are held constant. The unidirectional skill embedding model, the skill matching model, and the low-level policies are trained together in an end-to-end fashion in a second training.

公开(公告)号：US20240046127A1

公开(公告)日：2024-02-08

申请号：US18471558

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

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.