18681763. System and Methods for Training Machine-Learned Models for Use in Computing Environments with Limited Resources simplified abstract (Google LLC)
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 18681763 titled 'System and Methods for Training Machine-Learned Models for Use in Computing Environments with Limited Resources
The present disclosure introduces methods, systems, and devices for efficient training of models for embedded systems. The system accesses unlabeled data elements, trains encoder models for data encoding, generates encoded versions of labeled data elements, trains decoder models for label generation, and generates 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
Potential Applications: This technology can be applied in various fields such as machine learning, artificial intelligence, data processing, and embedded systems development.
Problems Solved: This technology addresses the challenges of efficiently training models for embedded systems, handling unlabeled data elements, and generating provisional labels for accurate model training.
Benefits: The benefits of this technology include improved model training efficiency, accurate label generation, and enhanced performance of embedded systems.
Commercial Applications: Title: "Efficient Model Training System for Embedded Systems" This technology can be utilized in industries such as IoT, robotics, autonomous vehicles, and smart devices to enhance the performance and efficiency of embedded systems.
Prior Art: Researchers can explore prior art related to machine learning algorithms, encoder-decoder models, and embedded systems development to understand the evolution of this technology.
Frequently Updated Research: Researchers can stay updated on advancements in machine learning algorithms, encoder-decoder models, and embedded systems development to enhance their understanding of this technology.
Questions about Efficient 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 applications of this technology in various 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.
