3results about How to "Has generalization ability" patented technology

This invention discloses an expert relearning reasoning question-answering method combined with a gating network. It uses a pre-trained language model for scoring and preprocessing to obtain the question context and reasoning graph. Finally, through an expert network, it deeply learns the semantic features of two representation forms and dynamically adjusts the contribution of the two representation forms to answer prediction using a gating network. Experimental results demonstrate that the proposed method is effective for question-answer prediction. By comprehensively considering the different contributions of the question text representation and the reasoned knowledge graph representation to answer prediction, the prediction accuracy can be improved. Experiments on public datasets also show good results, indicating that the method is not only applicable to fixed question-answer prediction but also has a certain generalization ability.

This invention discloses a method for identifying multi-feature interaction patterns in medical insurance fraud, comprising: constructing a RuleFit model based on gradient boosting decision trees (GWO) according to medical insurance data, wherein the GWO generates composite fraud rules, linear regression fitting generates linear fraud rules and rule quantification indicators, and an initial fraud rule set is obtained; optimizing the fraud rule set based on the Grey Wolf Optimization Algorithm (GWO), mining the optimal synergistic relationship between rules through multi-path parallel search, and iteratively filtering to obtain the optimal fraud rule set; constructing a 0-1 rule matrix and binary classification target labels based on the optimal fraud rule set, and classifying and identifying medical insurance fraud behavior through logistic regression. This invention, based on RuleFit, can generate a fraud rule set reflecting feature correlation; based on GWO, it achieves multi-path parallel search within the solution space composed of the full set of candidate rules, fully exploring the interaction and combination potential between rules.

This invention discloses a method, apparatus, and storage medium for 3D facial keypoint detection. The 3D facial keypoint detection method includes: acquiring original input data to characterize 3D face shape and texture data; uniformly sampling the original input data using a point cloud mesh to generate first sampled data; filling the first sampled data to generate first filled data; using a graph convolutional network to output a probability distribution graph model of 3D facial keypoints; and predicting the positions of 3D facial keypoints based on the probability distribution graph model. This invention proposes a transformation from the 3D facial keypoint probability graph model to keypoint positions, which can be adapted to the training of graph convolutional networks, improving the robustness and accuracy of the training results. Furthermore, this invention proposes a standardized representation method for 3D face geometry and texture data, which allows the training network to adapt to different types of data, thus possessing cross-dataset generalization ability.