View source for International business machines corporation (20240135227). GENERATING IN-DISTRIBUTION SAMPLES OF TIME-SERIES OR IMAGE DATA FOR THE NEIGHBORHOOD DISTRIBUTION simplified abstract

=GENERATING IN-DISTRIBUTION SAMPLES OF TIME-SERIES OR IMAGE DATA FOR THE NEIGHBORHOOD DISTRIBUTION=


==Organization Name==

[[:Category:international business machines corporation|international business machines corporation]]


[[Category:international business machines corporation]]

==Inventor(s)==

[[:Category:Natalia Martinez Gil of Durham NC (US)|Natalia Martinez Gil of Durham NC (US)]][[Category:Natalia Martinez Gil of Durham NC (US)]]

[[:Category:Kanthi Sarpatwar of Elmsford NY (US)|Kanthi Sarpatwar of Elmsford NY (US)]][[Category:Kanthi Sarpatwar of Elmsford NY (US)]]

[[:Category:Roman Vaculin of Larchmont NY (US)|Roman Vaculin of Larchmont NY (US)]][[Category:Roman Vaculin of Larchmont NY (US)]]

==GENERATING IN-DISTRIBUTION SAMPLES OF TIME-SERIES OR IMAGE DATA FOR THE NEIGHBORHOOD DISTRIBUTION - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240135227 titled 'GENERATING IN-DISTRIBUTION SAMPLES OF TIME-SERIES OR IMAGE DATA FOR THE NEIGHBORHOOD DISTRIBUTION

==Simplified Explanation==

The patent application describes a method, system, and computer program product for generating in-distribution samples of data for a neighborhood distribution to be used by post-hoc local explanation methods. An autoencoder is trained to generate in-distribution samples of input data for the neighborhood distribution to be used by a post-hoc local explanation method. The training process involves mapping the input data into a latent dimension, forming a first and a second latent code. A mixed code is then obtained by convexly combining the first and second latent codes with a random coefficient. The mixed code is decoded with the input data masked with interpretable features to obtain conditional mixed reconstructions. Adversarial training is then performed against a discriminator to promote in-distribution samples by computing the reconstruction losses of the conditional mixed reconstructions as well as the discriminator losses and then minimizing such losses.

* Autoencoder trained to generate in-distribution samples of input data
* Mapping input data into a latent dimension to form latent codes
* Obtaining a mixed code by combining latent codes with a random coefficient
* Decoding mixed code with input data masked with interpretable features
* Adversarial training against a discriminator to promote in-distribution samples

== Potential Applications ==
This technology could be applied in various fields such as anomaly detection, fraud detection, and predictive maintenance.

== Problems Solved ==
This technology helps in generating in-distribution samples of data for neighborhood distributions, which can improve the performance of post-hoc local explanation methods.

== Benefits ==
The benefits of this technology include improved interpretability of machine learning models, enhanced accuracy in local explanations, and better understanding of model behavior.

== Potential Commercial Applications ==
One potential commercial application of this technology could be in the financial sector for fraud detection systems.

== Possible Prior Art ==
Prior art in the field of generative models and adversarial training techniques may exist, but specific examples are not provided in the patent application.

=== Unanswered Questions ===

=== How does this technology compare to existing methods for generating in-distribution samples of data? ===
This article does not provide a direct comparison with existing methods, leaving the reader to wonder about the specific advantages of this approach over others.

=== What are the limitations or potential drawbacks of using this technology in practice? ===
The article does not address any potential limitations or drawbacks of implementing this technology, leaving room for uncertainty about its practical implications.


==Original Abstract Submitted==

a computer-implemented method, system and computer program product for generating in-distribution samples of data for a neighborhood distribution to be used by post-hoc local explanation methods. an autoencoder is trained to generate in-distribution samples of input data for the neighborhood distribution to be used by a post-hoc local explanation method. such training includes mapping the input data (e.g., time series data) into a latent dimension (or latent space) forming a first and a second latent code. a mixed code is then obtained by convexly combining the first and second latent codes with a random coefficient. the mixed code is then decoded with the input data masked with interpretable features to obtain conditional mixed reconstructions. adversarial training is then performed against a discriminator in order to promote in-distribution samples by computing the reconstruction losses of the conditional mixed reconstructions as well as the discriminator losses and then minimizing such losses.



[[Category:G06N20/00]]