Google llc (20240338572). System and Methods for Training Machine-Learned Models for Use in Computing Environments with Limited Resources simplified abstract
System and Methods for Training Machine-Learned Models for Use in Computing Environments with Limited Resources
Organization Name
Inventor(s)
Nicholas Gillian of Palo Alto CA (US)
Lawrence Au of Sunnyvale CA (US)
System and Methods for Training Machine-Learned Models for Use in Computing Environments with Limited Resources - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240338572 titled 'System and Methods for Training Machine-Learned Models for Use in Computing Environments with Limited Resources
The present disclosure describes a system for training models for use in embedded systems by accessing unlabeled data elements, training encoder models for data encoding, generating encoded versions of labeled data elements, training decoder models for label generation, and generating provisional labels for unlabeled data elements.
- Efficient training of models for embedded systems
- Accessing unlabeled data elements
- Training encoder models for data encoding
- Generating encoded versions of labeled data elements
- Training decoder models for label generation
- Generating provisional labels for unlabeled data elements
- Training student models using unlabeled data elements and provisional labels
Potential Applications: This technology can be applied in various industries such as manufacturing, healthcare, finance, and transportation for optimizing processes and improving decision-making based on data analysis.
Problems Solved: This technology addresses the challenge of efficiently training models for embedded systems without the need for extensive labeled data sets, reducing the time and resources required for model development.
Benefits: The benefits of this technology include faster model training, improved accuracy in data encoding and label generation, and increased efficiency in utilizing unlabeled data elements for model development.
Commercial Applications: Title: "Efficient Model Training System for Embedded Systems" This technology can be commercially used in industries that rely on embedded systems, such as IoT devices, autonomous vehicles, and industrial automation, to enhance performance and reliability through optimized model training processes.
Prior Art: Researchers can explore prior art related to machine learning algorithms, data encoding techniques, and model training systems for embedded systems to understand the evolution of similar technologies in the field.
Frequently Updated Research: Stay updated on advancements in machine learning algorithms, data encoding methods, and model training techniques for embedded systems to leverage the latest innovations in the industry.
Questions about Model Training System for Embedded Systems: 1. How does this technology improve the efficiency of model training for embedded systems? 2. What are the potential challenges in implementing this system in different industries?
Original Abstract Submitted
the present disclosure provides computer-implemented methods, systems, and devices for efficient training of models for use in embedded systems. a model training system accesses unlabeled data elements. the model training system trains one or more encoder models for data encoding of using each unlabeled data element as input. the model training system generates an encoded version of each of a plurality of labeled data elements. the model training system trains decoder models for label generation using the encoded version of the second data set as input. the model training system generates provisional labels for the unlabeled data elements in the first data set, such that each unlabeled data element has an associated provisional label. the model training system trains one or more student models using the unlabeled data elements from the first data set and the associated provisional labels.
