18679190. Debugging Correctness Issues in Training Machine Learning Models simplified abstract (Google LLC)
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 18679190 titled 'Debugging Correctness Issues in Training Machine Learning Models
Simplified Explanation: The patent application describes a method that involves training machine learning models on different computing systems with different configurations and architectures. The method includes determining a similarity measure between the training outputs of the two systems during a shared training operation and displaying a graphical representation based on this measure to the user.
Key Features and Innovation:
- Training machine learning models on different computing systems with varying configurations.
- Determining a similarity measure between training outputs of different systems during a shared training operation.
- Displaying a graphical representation based on the similarity measure to the user.
Potential Applications: This technology can be applied in various fields such as:
- Machine learning research and development.
- Data analysis and pattern recognition.
- Predictive modeling and forecasting.
Problems Solved:
- Addressing the challenge of comparing training outputs from different computing systems.
- Enhancing the understanding of the similarities between machine learning models trained on different configurations.
Benefits:
- Improved model evaluation and comparison.
- Enhanced collaboration between different computing systems.
- Better insights into the training process of machine learning models.
Commercial Applications: Potential commercial applications include:
- Software development for machine learning platforms.
- Data analytics services for businesses.
- Research and development in artificial intelligence.
Prior Art: Readers interested in prior art related to this technology can explore research papers, patents, and publications in the field of machine learning model training and evaluation.
Frequently Updated Research: Stay updated on the latest advancements in machine learning model training and evaluation techniques to leverage the full potential of this technology.
Questions about Machine Learning Model Training and Evaluation: 1. What are the key challenges in comparing training outputs from different computing systems? 2. How does determining a similarity measure between training outputs enhance the training process of machine learning models?
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.
