公开(公告)号：US20220076135A1

公开(公告)日：2022-03-10

申请号：US17391526

申请日：2021-08-02

Applicant: NEC Laboratories America, Inc.

Inventor： Zhengzhang Chen ,  Haifeng Chen ,  Yuening Li

IPC: G06N3/08 ,  G06N5/02 ,  G06N20/00

Abstract: A method for employing meta-learning based feature disentanglement to extract transferrable knowledge in an unsupervised setting is presented. The method includes identifying how to transfer prior knowledge data from a plurality of source domains to one or more target domains, extracting domain dependence features and domain agnostic features from the prior knowledge data, via a disentangle meta-controller, by discovering factors of variation within the prior knowledge data received from a data stream, and obtaining an evaluation for a downstream task, via a child network, to obtain an optimal child model and a feature disentangle strategy.

公开(公告)号：US20220253696A1

公开(公告)日：2022-08-11

申请号：US17582191

申请日：2022-01-24

Applicant: NEC Laboratories America, Inc.

Inventor： Zhengzhang Chen ,  Haifeng Chen ,  Yuening Li

IPC: G06N3/08 ,  G06N3/04

Abstract: A method for employing a deep unsupervised generative approach for disentangled factor learning is presented. The method includes decomposing, via an individual factor disentanglement component, latent variables into independent factors having different semantic meaning, enriching, via a group segment disentanglement component, group-level semantic meaning of sequential data by grouping the sequential data into a batch of segments, and generating hierarchical semantic concepts as interpretable and disentangled representations of time series data.

公开(公告)号：US20210256392A1

公开(公告)日：2021-08-19

申请号：US17170254

申请日：2021-02-08

Applicant: NEC Laboratories America, Inc.

Inventor： Zhengzhang Chen ,  Haifeng Chen ,  Yuening Li

IPC: G06N3/08 ,  G06N3/04

Abstract: Systems and methods for automatically generating a neural network to perform anomaly detection. The method includes defining a search space, including parameters for neural network architectures, definition-hypothesis of an anomaly assumption, and loss functions, as a tuple, and selecting a first candidate anomaly detection architecture from the search space that defines the parameters of the neural network architecture. The method further includes feeding a data set into the neural network defined by the first and second candidate anomaly detection architectures, and selecting a second candidate anomaly detection architecture from the search space that defines the parameters of the neural network. The method further includes determining a performance difference between the first architecture and the second architecture. The method further includes repeating the defining of the neural network with subsequent candidates, and identifying a best neural network candidate from the search space based on the performance differences.