Google llc (20240320566). Debugging Correctness Issues in Training Machine Learning Models simplified abstract
Contents
Debugging Correctness Issues in Training Machine Learning Models
Organization Name
Inventor(s)
Chi Keung Luk of Fremont CA (US)
Jose Americo Baiocchi Paredes of Mountain View CA (US)
Russell Power of Seattle WA (US)
Mehmet Deveci of Santa Clara CA (US)
Debugging Correctness Issues in Training Machine Learning Models - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240320566 titled 'Debugging Correctness Issues in Training Machine Learning Models
The patent application describes a method that involves training machine learning models on different computing systems with different configurations.
- The method includes determining a similarity measure between training outputs generated by the different computing systems during shared training operations.
- A graphical representation based on the similarity measure is displayed to the user.
- This approach allows for comparing the training outputs of machine learning models trained on different systems.
Potential Applications:
- This technology can be applied in various industries where machine learning models are used for decision-making processes.
- It can be beneficial in research settings where comparing the performance of machine learning models is essential.
Problems Solved:
- Addressing the challenge of comparing training outputs of machine learning models trained on different computing systems.
- Providing a visual representation of the similarity between training outputs for easier analysis.
Benefits:
- Enhanced understanding of the performance of machine learning models trained on different systems.
- Improved decision-making based on the comparison of training outputs.
Commercial Applications:
- "Enhanced Machine Learning Model Comparison Method for Industry Applications"
- This technology can be utilized in sectors such as healthcare, finance, and manufacturing for optimizing machine learning model performance.
Questions about Enhanced Machine Learning Model Comparison Method: 1. How does this method improve the efficiency of comparing machine learning models trained on different systems?
- This method allows for a quantitative comparison of training outputs, providing a more objective assessment of model performance.
2. What are the potential implications of using this technology in industries that rely on machine learning models?
- Industries can benefit from improved decision-making processes and optimized model performance, leading to better outcomes.
Original Abstract Submitted
a method includes training, using a first computing system having a first configuration, a first machine learning model having a machine learning model architecture, and training, using a second computing system having a different second configuration, a second machine learning model having the machine learning model architecture. the method also includes determining, for a shared training operation performed by both the first computing system and the second computing system, a similarity measure that represents a similarity between: a first training output generated by the first computing system during performance of the shared training operation during training of the first machine learning model; and a second training output generated by the second computing system during performance of the shared training operation during training of the second machine learning model. the method further includes displaying, to a user, a graphical representation based on the similarity measure determined for the shared training operation.
