View source for 20240054347. SYSTEM AND METHOD OF TRAINING A NEURAL NETWORK MODEL simplified abstract (TECHNION RESEARCH & DEVELOPMENT FOUNDATION LIMITED)

=SYSTEM AND METHOD OF TRAINING A NEURAL NETWORK MODEL=


==Organization Name==

[[:Category:TECHNION RESEARCH & DEVELOPMENT FOUNDATION LIMITED|TECHNION RESEARCH & DEVELOPMENT FOUNDATION LIMITED]]


[[Category:TECHNION RESEARCH & DEVELOPMENT FOUNDATION LIMITED]]

==Inventor(s)==

[[:Category:Guy Gilboa of Haifa (IL)|Guy Gilboa of Haifa (IL)]][[Category:Guy Gilboa of Haifa (IL)]]

[[:Category:Rotem Turjeman of Haifa (IL)|Rotem Turjeman of Haifa (IL)]][[Category:Rotem Turjeman of Haifa (IL)]]

[[:Category:Tom Berkov of Haifa (IL)|Tom Berkov of Haifa (IL)]][[Category:Tom Berkov of Haifa (IL)]]

[[:Category:Ido Cohen of Haifa (IL)|Ido Cohen of Haifa (IL)]][[Category:Ido Cohen of Haifa (IL)]]

==SYSTEM AND METHOD OF TRAINING A NEURAL NETWORK MODEL - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240054347 titled 'SYSTEM AND METHOD OF TRAINING A NEURAL NETWORK MODEL

==Simplified Explanation==

The patent application describes a method and system for implementing a machine-learning based function using a neural network model.

* Providing a neural network model with multiple parameters.
* Training the neural network model over multiple epochs using a training dataset to implement a predefined machine learning function.
* Calculating a profile vector for each neural network parameter to represent its evolution through the training epochs.
* Approximating the value of each neural network parameter based on the profile vector.
* Replacing the original parameter values in the trained neural network model with the calculated approximated values to obtain an approximated version of the model.

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### Potential Applications
* Improving the efficiency and accuracy of machine learning models.
* Enhancing the performance of neural networks in various applications such as image recognition, natural language processing, and predictive analytics.

### Problems Solved
* Overfitting of neural network models.
* Reducing the computational resources required for training neural networks.
* Improving the interpretability of neural network models.

### Benefits
* Increased accuracy and reliability of machine learning models.
* Faster training times for neural networks.
* Better understanding of the behavior of neural network parameters.


==Original Abstract Submitted==

a method and system for implementing a machine-learning (ml) based function may include providing a nn model comprising a plurality of nn parameters; training the nn model over a plurality of training epochs, to implement a predefined ml function, based on a training dataset; for one or more nn parameters of the plurality of nn parameters: (i) calculating a profile vector, representing evolution of the nn parameter through the plurality of training epochs; and (ii) calculating an approximated value of the at least one nn parameter, based on the profile vector; and replacing at least one nn parameter value in the trained nn model with a respective calculated approximated value, to obtain an approximated version of the trained nn model.



[[Category:G06N3/084]]
[[Category:G06N3/0464]]