17951587. TRAINING NEURAL NETWORKS WITH CONVERGENCE TO A GLOBAL MINIMUM simplified abstract (International Business Machines Corporation)
Contents
TRAINING NEURAL NETWORKS WITH CONVERGENCE TO A GLOBAL MINIMUM
Organization Name
International Business Machines Corporation
Inventor(s)
Lam Minh Nguyen of Ossining NY (US)
TRAINING NEURAL NETWORKS WITH CONVERGENCE TO A GLOBAL MINIMUM - A simplified explanation of the abstract
This abstract first appeared for US patent application 17951587 titled 'TRAINING NEURAL NETWORKS WITH CONVERGENCE TO A GLOBAL MINIMUM
Simplified Explanation
The patent application describes a method for training a neural network with a non-convex architecture by approximating a convex optimization sub-problem to learn a common classifier from training data.
- Initial weight vector selection: Choose an initial weight vector for the convex optimization sub-problem associated with the neural network.
- Approximation and updating: Use at least one processor to approximate a solution to the convex optimization sub-problem, update the initial weight vector by subtracting the approximate solution times a learning rate, and repeat for multiple iterations.
- Convergence to global minimum: Continue iterating until convergence to a global minimum is achieved, obtaining a final weight vector for the neural network to implement the common classifier.
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- Potential Applications
This technology can be applied in various fields such as image recognition, natural language processing, and autonomous driving for developing efficient and accurate classifiers.
- Problems Solved
This technology addresses the challenge of training neural networks with non-convex architectures by approximating convex optimization sub-problems, leading to improved convergence and performance.
- Benefits
The benefits of this technology include faster training times, improved accuracy of classifiers, and the ability to handle complex neural network architectures effectively.
- Potential Commercial Applications
This technology can be utilized in industries such as healthcare for medical image analysis, finance for fraud detection, and e-commerce for personalized recommendations, offering enhanced machine learning capabilities.
- Possible Prior Art
Prior art may include techniques for training neural networks using gradient descent, stochastic gradient descent, or other optimization algorithms to minimize loss functions and improve model performance.
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- Unanswered Questions
- How does this method compare to other approaches for training neural networks with non-convex architectures?
The article does not provide a comparison with other methods for training neural networks with non-convex architectures, such as genetic algorithms or reinforcement learning.
- What are the computational requirements of implementing this method on large-scale datasets?
The article does not discuss the computational resources needed to apply this method to training neural networks on extensive datasets, which could be crucial for real-world applications.
Original Abstract Submitted
Select an initial weight vector for a convex optimization sub-problem associated with a neural network having a non-convex network architecture loss surface. With at least one processor, approximate a solution to the convex optimization sub-problem that obtains a search direction, to learn a common classifier from training data. With the at least one processor, update the initial weight vector by subtracting the approximate solution to the convex optimization sub-problem times a first learning rate. With the at least one processor, repeat the approximating and updating steps, for a plurality of iterations, with the updated weight vector from a given one of the iterations taken as the initial weight vector for a next one of the iterations, to obtain a final weight vector for the neural network, until convergence to a global minimum is achieved, to implement the common classifier.
