View source for Deepmind technologies limited (20240256861). DETERMINING STATIONARY POINTS OF A LOSS FUNCTION USING CLIPPED AND UNBIASED GRADIENTS simplified abstract

=DETERMINING STATIONARY POINTS OF A LOSS FUNCTION USING CLIPPED AND UNBIASED GRADIENTS=


==Organization Name==

[[:Category:deepmind technologies limited|deepmind technologies limited]]


[[Category:deepmind technologies limited]]

==Inventor(s)==

[[:Category:Marcus Hutter of London (GB)|Marcus Hutter of London (GB)]][[Category:Marcus Hutter of London (GB)]]

[[:Category:Bryn Hayeder Khalid Elesedy of Enfield (GB)|Bryn Hayeder Khalid Elesedy of Enfield (GB)]][[Category: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.



[[Category:G06N3/08]]
[[Category:CPC_G06N3/08]]