Deepmind technologies limited (20240256861). DETERMINING STATIONARY POINTS OF A LOSS FUNCTION USING CLIPPED AND UNBIASED GRADIENTS simplified abstract
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DETERMINING STATIONARY POINTS OF A LOSS FUNCTION USING CLIPPED AND UNBIASED GRADIENTS
Organization Name
Inventor(s)
Bryn Hayeder Khalid Elesedy of Enfield (GB)
DETERMINING STATIONARY POINTS OF A LOSS FUNCTION USING CLIPPED AND UNBIASED GRADIENTS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240256861 titled 'DETERMINING STATIONARY POINTS OF A LOSS FUNCTION USING CLIPPED AND UNBIASED GRADIENTS
The abstract describes a method for optimizing a loss function defined by numerical parameters through a series of training iterations involving gradient calculations and clipping techniques.
- The method involves determining initial parameter values and performing multiple training iterations.
- Each iteration includes calculating the gradient of the loss function, combining it with a clipped value from the previous iteration, and processing the modified gradient using a clipping function.
- The clipped gradient is used to update the parameter values, and the difference between the modified gradient and the clipped gradient is stored for the next iteration.
Potential Applications: - Machine learning algorithms - Optimization problems in various industries - Financial modeling and analysis
Problems Solved: - Efficient optimization of loss functions - Improved convergence of training algorithms - Enhanced performance of numerical parameter optimization
Benefits: - Faster convergence in training iterations - More stable optimization process - Better performance of machine learning models
Commercial Applications: Title: "Advanced Optimization Method for Machine Learning Models" This technology can be used in: - Data analytics companies - Financial institutions - E-commerce platforms
Questions about the technology: 1. How does this method compare to traditional optimization techniques in terms of efficiency? 2. What are the potential limitations of using clipping functions in gradient processing?
Frequently Updated Research: Stay updated on advancements in machine learning optimization techniques and their applications in various industries.
Original Abstract Submitted
a method of optimizing a loss function defined by one or more numerical parameters is provided. the method comprises determining initial values of the parameters, and performing a plurality of training iterations. each training iteration except the first comprises (i) determining a gradient of the loss function associated with the parameters, (ii) obtaining a clipped value generated in a previous training iteration, (iii) additively combining the gradient and the clipped value to generate a modified gradient, (iv) processing, using a clipping function based on a threshold value, the modified gradient to generate a clipped gradient, (v) updating the value of the one or more parameters based on the clipped gradient, and (vi) storing, as the clipped value for use in a next training iteration, a difference between the modified gradient and the clipped gradient.
