Apple inc. (20240296346). MUTABLE PARAMETERS FOR MACHINE LEARNING MODELS DURING RUNTIME simplified abstract
Contents
MUTABLE PARAMETERS FOR MACHINE LEARNING MODELS DURING RUNTIME
Organization Name
Inventor(s)
Cecile M. Foret of Palo Alto CA (US)
Xiaozhong Yao of Cupertino CA (US)
Sundararaman Hariharasubramanian of Santa Clara CA (US)
MUTABLE PARAMETERS FOR MACHINE LEARNING MODELS DURING RUNTIME - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240296346 titled 'MUTABLE PARAMETERS FOR MACHINE LEARNING MODELS DURING RUNTIME
The subject technology involves receiving code for a neural network (NN) model and a set of weights for the model. It determines which layers in the NN model are mutable and provides information for mapping a second set of weights to the original set. Metadata is generated to enable updating the mutable layers and weights during the execution of the NN model.
- The technology identifies mutable layers in a neural network model.
- It provides information for mapping a second set of weights to the original set.
- Metadata is generated to facilitate updating the mutable layers and weights during execution.
- This innovation allows for dynamic adjustments to the neural network model.
- It enhances the flexibility and adaptability of neural network models.
Potential Applications: This technology can be applied in various fields such as image recognition, natural language processing, and autonomous vehicles.
Problems Solved: The technology addresses the need for real-time adjustments in neural network models without the need for retraining.
Benefits: - Improved performance and accuracy in neural network models. - Enhanced adaptability to changing data and conditions. - Increased efficiency in model optimization.
Commercial Applications: Title: Dynamic Neural Network Model Optimization This technology can be utilized in industries such as healthcare, finance, and cybersecurity for real-time data analysis and decision-making processes.
Prior Art: Researchers can explore existing patents related to neural network model optimization and dynamic weight adjustments.
Frequently Updated Research: Stay updated on advancements in neural network optimization techniques and real-time model adjustments for improved performance and efficiency.
Questions about Dynamic Neural Network Model Optimization: 1. How does this technology compare to traditional methods of neural network model optimization? 2. What are the potential limitations of dynamically adjusting weights in a neural network model?
Original Abstract Submitted
the subject technology receives code corresponding to a neural network (nn) model and a set of weights for the nn model. the subject technology determines a set of layers that are mutable in the nn model. the subject technology determines information for mapping a second set of weights to the set of weights for the nn model. the subject technology generates metadata corresponding to the set of layers that are mutable, and the information for mapping the second set of weights to the set of weights for the nn model, wherein the generated metadata enables updating the set of layers that are mutable during execution of the nn model.
