Xu U Chen, 6027 Tuscarora Ln, Clayton, NC 27520

2021035, Nov 18, 2021

Jul 28, 2021

17/386706

- Cary NC, US
Xu Chen - Cary NC, US

G06K 9/62
G06N 3/08

A computing device classifies unclassified observations. A first batch of unclassified observation vectors and a first batch of classified observation vectors are selected. A prior regularization error value and a decoder reconstruction error value are computed. A first batch of noise observation vectors is generated. An evidence lower bound (ELBO) value is computed. A gradient of an encoder neural network model is computed, and the ELBO value is updated. A decoder neural network model and an encoder neural network model are updated. The decoder neural network model is trained. The target variable value is determined for each observation vector of the unclassified observation vectors based on an output of the trained decoder neural network model. The target variable value is output.

2020037, Nov 26, 2020

May 21, 2020

16/880551

- Cary NC, US
Xu Chen - Cary NC, US

G06N 7/00
G06N 20/20

A computing system trains a clustering model. A responsibility parameter vector is initialized for each observation vector and includes a probability value of a cluster membership. The observation vectors include a plurality of classified observation vectors and a plurality of unclassified observation vectors. (A) Beta distribution parameter values are computed for each cluster. (B) Parameter values are computed for a normal-Wishart distribution for each cluster. (C) Each responsibility parameter vector is updated using the beta distribution parameter values, the parameter values, and a respective observation vector. (D) A convergence parameter value is computed. (E) (A) to (D) are repeated until the computed convergence parameter value indicates the responsibility parameter vector defined for each observation vector of the plurality of unclassified observation vectors is converged. A cluster membership is determined and output for each observation vector using a respective, updated responsibility parameter vector.

2019032, Oct 24, 2019

May 1, 2019

16/400157

- Cary NC, US
Xu Chen - Apex NC, US

G06K 9/62
G06N 20/10
G06F 17/18

A computing device predicts an event or classifies an observation. A trained labeling model is executed with unlabeled observations to define a label distribution probability matrix. A label is selected for each observation. A mean observation vector and a covariance matrix are computed from the unlabeled observations selected to have each respective label. A number of eigenvalues that have a smallest value is selected from each covariance matrix and used to define a null space for each respective label. A distance value is computed for a distance vector computed to the mean observation vector and projected into the null space associated with the label selected for each respective observation. A diversity rank is determined for each respective observation based on minimum computed distance values. A predefined number of observations having highest values for the diversity rank are included in labeled observations and removed from the unlabeled observations.