View source for UMNAI Limited (20240249143). METHOD FOR AN EXPLAINABLE AUTOENCODER AND AN EXPLAINABLE GENERATIVE ADVERSARIAL NETWORK simplified abstract

=METHOD FOR AN EXPLAINABLE AUTOENCODER AND AN EXPLAINABLE GENERATIVE ADVERSARIAL NETWORK=


==Organization Name==

[[:Category:UMNAI Limited|UMNAI Limited]]


[[Category:UMNAI Limited]]

==Inventor(s)==

[[:Category:Angelo Dalli of Floriana (MT)|Angelo Dalli of Floriana (MT)]][[Category:Angelo Dalli of Floriana (MT)]]

[[:Category:Mauro Pirrone of Kalkara (MT)|Mauro Pirrone of Kalkara (MT)]][[Category:Mauro Pirrone of Kalkara (MT)]]

[[:Category:Matthew Grech of San Gwann (MT)|Matthew Grech of San Gwann (MT)]][[Category:Matthew Grech of San Gwann (MT)]]

==METHOD FOR AN EXPLAINABLE AUTOENCODER AND AN EXPLAINABLE GENERATIVE ADVERSARIAL NETWORK - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240249143 titled 'METHOD FOR AN EXPLAINABLE AUTOENCODER AND AN EXPLAINABLE GENERATIVE ADVERSARIAL NETWORK

'''Simplified Explanation''': 
The patent application describes an explainable autoencoder that can attribute each feature of the input to the output, making it suitable for classification tasks and other machine learning scenarios.

* Provides an autoencoder that explains the contribution of each input feature to the output.
* Can be used for classification tasks like anomaly detection.
* Suitable for scenarios where the autoencoder is part of a larger machine learning system.
* Incorporates explainable generation, simulation, and discrimination capabilities.
* Based on an interpretable neural network architecture for full explainability.

'''Key Features and Innovation''':
* Explainable autoencoder that attributes input features to output.
* Suitable for classification tasks and anomaly detection.
* Incorporates explainable generation, simulation, and discrimination capabilities.
* Based on an interpretable neural network architecture for full explainability.

'''Potential Applications''':
* Anomaly detection in various industries.
* Classification tasks in machine learning systems.
* Integration into end-to-end deep learning architectures.

'''Problems Solved''':
* Lack of transparency in traditional autoencoder models.
* Difficulty in understanding the contribution of input features to the output.

'''Benefits''':
* Improved transparency and interpretability in machine learning models.
* Enhanced understanding of feature contributions in classification tasks.
* Better integration into complex machine learning systems.

'''Commercial Applications''':
Autoencoder for Explainable Machine Learning Systems

'''Questions about Explainable Autoencoders''':
1. How does the explainable autoencoder improve transparency in machine learning models?
2. What are the potential applications of explainable autoencoders in real-world scenarios?


==Original Abstract Submitted==

an exemplary embodiment provides an autoencoder which is explainable. an exemplary autoencoder may explain the degree to which each feature of the input attributed to the output of the system, which may be a compressed data representation. an exemplary embodiment may be used for classification, such as anomaly detection, as well as other scenarios where an autoencoder is input to another machine learning system or when an autoencoder is a component in an end-to-end deep learning architecture. an exemplary embodiment provides an explainable generative adversarial network that adds explainable generation, simulation and discrimination capabilities. the underlying architecture of an exemplary embodiment may be based on an explainable or interpretable neural network, allowing the underlying architecture to be a fully explainable white-box machine learning system.



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