20240013064. MACHINE LEARNING TECHNIQUES USING MODEL DEFICIENCY DATA OBJECTS FOR TENSOR-BASED GRAPH PROCESSING MODELS simplified abstract (Optum, Inc.)
MACHINE LEARNING TECHNIQUES USING MODEL DEFICIENCY DATA OBJECTS FOR TENSOR-BASED GRAPH PROCESSING MODELS
Organization Name
Inventor(s)
Erik A. Nystrom of Durham NC (US)
Gregory J. Boss of Saginaw MI (US)
MACHINE LEARNING TECHNIQUES USING MODEL DEFICIENCY DATA OBJECTS FOR TENSOR-BASED GRAPH PROCESSING MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240013064 titled 'MACHINE LEARNING TECHNIQUES USING MODEL DEFICIENCY DATA OBJECTS FOR TENSOR-BASED GRAPH PROCESSING MODELS
Simplified Explanation
The present invention relates to methods, apparatus, systems, computing devices, computing entities, and/or the like for generating a model deficiency data object for a tensor-based graph processing machine learning model. The invention utilizes systems, methods, and computer program products that generate a model deficiency data object for a tensor-based graph processing machine learning model using holistic graph links generated by utilizing a graph representation machine learning model.
- The invention provides a method for generating a model deficiency data object for a tensor-based graph processing machine learning model.
- The method utilizes holistic graph links generated by utilizing a graph representation machine learning model.
- The generated model deficiency data object helps in identifying and addressing deficiencies in the machine learning model.
- The invention can be implemented using various apparatus, systems, computing devices, and computing entities.
- The invention can be implemented using computer program products that facilitate the generation of the model deficiency data object.
- The tensor-based graph processing machine learning model can benefit from the generated model deficiency data object to improve its performance and accuracy.
Potential applications of this technology:
- Improving the performance and accuracy of tensor-based graph processing machine learning models.
- Identifying and addressing deficiencies in machine learning models.
- Enhancing the capabilities of graph representation machine learning models.
- Optimizing the training and learning process of tensor-based graph processing machine learning models.
Problems solved by this technology:
- Lack of efficient methods for identifying and addressing deficiencies in machine learning models.
- Difficulty in optimizing the training and learning process of tensor-based graph processing machine learning models.
- Inadequate capabilities of graph representation machine learning models in generating holistic graph links.
Benefits of this technology:
- Improved performance and accuracy of tensor-based graph processing machine learning models.
- Enhanced capabilities of graph representation machine learning models.
- Efficient identification and resolution of deficiencies in machine learning models.
- Optimized training and learning process for tensor-based graph processing machine learning models.
Original Abstract Submitted
various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating a model deficiency data object for a tensor-based graph processing machine learning model. certain embodiments of the present invention utilize systems, methods, and computer program products that generate a model deficiency data object for a tensor-based graph processing machine learning model using holistic graph links generated by utilizing a graph representation machine learning model.
