Samsung electronics co., ltd. (20240330685). METHOD AND APPARATUS FOR GENERATING A NOISE-RESILIENT MACHINE LEARNING MODEL simplified abstract
Contents
METHOD AND APPARATUS FOR GENERATING A NOISE-RESILIENT MACHINE LEARNING MODEL
Organization Name
Inventor(s)
Timothy Hospedales of Staines (GB)
METHOD AND APPARATUS FOR GENERATING A NOISE-RESILIENT MACHINE LEARNING MODEL - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240330685 titled 'METHOD AND APPARATUS FOR GENERATING A NOISE-RESILIENT MACHINE LEARNING MODEL
The present application describes a computer-implemented method for optimizing the loss function during deep learning.
- Receiving a training data set with multiple data items.
- Initializing weights of at least one neural network layer of the ML model.
- Training the neural network layer using an iterative process with the data items.
- Optimizing the loss function by minimizing both loss value and loss sharpness simultaneously.
- Updating the weights of the neural network layer based on the optimized loss function.
Potential Applications: - Improving the efficiency and accuracy of deep learning models. - Enhancing the performance of machine learning algorithms in various fields such as image recognition, natural language processing, and predictive analytics.
Problems Solved: - Addressing the challenge of optimizing the loss function effectively during deep learning. - Improving the training process of neural network layers to achieve better results.
Benefits: - Increased accuracy and efficiency in deep learning models. - Enhanced performance of machine learning algorithms. - Faster convergence during training processes.
Commercial Applications: - This technology can be applied in industries such as healthcare, finance, e-commerce, and autonomous vehicles to improve data analysis and decision-making processes.
Prior Art: - Researchers and developers can explore prior studies on loss function optimization in deep learning models to understand the evolution of this technology.
Frequently Updated Research: - Stay updated on the latest advancements in loss function optimization techniques for deep learning models to incorporate cutting-edge methods into your projects.
Questions about Loss Function Optimization in Deep Learning: 1. How does optimizing the loss function improve the performance of deep learning models?
- Optimizing the loss function helps the model converge faster and achieve higher accuracy by adjusting the weights effectively.
2. What are some common challenges faced in optimizing the loss function during deep learning?
- Common challenges include balancing between minimizing loss value and loss sharpness, as well as avoiding overfitting or underfitting the model.
Original Abstract Submitted
the present application relates to a computer-implemented method for an improved technique for optimising the loss function during deep learning. the method includes receiving a training data set comprising a plurality of data items, initialising weights of at least one neural network layer of the ml model, and training, using an iterative process, the at least one neural network layer of the ml model by inputting, into the at least one neural network layer, the plurality of data items, processing the plurality of data items using the at least one neural network layer and the weights, optimising a loss function of the weights by simultaneously minimising a loss value and a loss sharpness using weights that lie in a neighbourhood having a similar low loss value, wherein the neighbourhood is determined by a geometry of a parameter space defined by the weights of the ml model, and updating the weights of the at least one neural network layer using the optimised loss function.
