公开(公告)号：US20240265304A1

公开(公告)日：2024-08-08

申请号：US18336538

申请日：2023-06-16

申请人： Optum, Inc.

发明人： Dominik Roman Christian DAHLEM ,  Eran HALPERIN ,  James ZOU ,  Kevin WU

IPC分类号： G06N20/00

CPC分类号： G06N20/00

摘要： Various embodiments of the present disclosure provide techniques for optimally augmenting a training dataset for a machine learning model based on multiple model-focused predictions. The techniques may include generating a datapoint priority matrix that corresponds to a plurality of entity-feature value pairs of a training dataset for a machine learning model, generating a plurality of impact predictions and feature sensitivity predictions for the plurality of entity-feature value pairs, generating a refined datapoint priority matrix by updating the datapoint priority matrix based on the plurality of impact predictions and sensitivity predictions, and providing a datapoint collection output for the training dataset based on the refined datapoint priority matrix and a data augmentation threshold.

公开(公告)号：US20240169185A1

公开(公告)日：2024-05-23

申请号：US18446971

申请日：2023-08-09

申请人： Optum, Inc.

发明人： Sanjit S. BATRA ,  Robert E. TILLMAN ,  Brian Lawrence Hill ,  Eran HALPERIN ,  Josue Ramon NASSAR

IPC分类号： G06N3/0455

CPC分类号： G06N3/0455

摘要： Embodiments of the present disclosure provide for improved data processing using interconnected variational autoencoder models, which may be used for any of a myriad of purposes. Some embodiments specially train the interconnected variational autoencoder models by utilizing different training scenarios corresponding to presence and/or absence of particular data in a training data set. Particular encoder(s) and/or decoder(s) from the specially trained interconnected variational autoencoder models may then be utilized to improve accuracy of the desired data processing tasks, for example, to generate particular output data.