View source for International business machines corporation (20240176988). GENERATING LOW-DISTORTION, IN-DISTRIBUTION NEIGHBORHOOD SAMPLES OF AN INSTANCE OF A DATASET USING A VARIATIONAL AUTOENCODER simplified abstract

=GENERATING LOW-DISTORTION, IN-DISTRIBUTION NEIGHBORHOOD SAMPLES OF AN INSTANCE OF A DATASET USING A VARIATIONAL AUTOENCODER=


==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 LOW-DISTORTION, IN-DISTRIBUTION NEIGHBORHOOD SAMPLES OF AN INSTANCE OF A DATASET USING A VARIATIONAL AUTOENCODER - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240176988 titled 'GENERATING LOW-DISTORTION, IN-DISTRIBUTION NEIGHBORHOOD SAMPLES OF AN INSTANCE OF A DATASET USING A VARIATIONAL AUTOENCODER

==Simplified Explanation==

The abstract describes a method, system, and computer program product for utilizing a variational autoencoder for neighborhood sampling. The variational autoencoder is trained to generate in-distribution neighborhood samples, which are then used to explain the predictions of a black box model.

* Variational autoencoder trained to generate in-distribution neighborhood samples
* In-distribution neighborhood samples generated in latent space satisfying a distortion constraint
* Interpretable examples generated using a k-nearest neighbors algorithm
* Interpretable examples used to explain black box model predictions
* Improved accuracy of post-hoc local explanation methods

== Potential Applications ==
This technology could be applied in various fields such as:
* Machine learning
* Data analysis
* Predictive modeling

== Problems Solved ==
This technology addresses the following issues:
* Lack of interpretability in black box models
* Difficulty in explaining complex model predictions
* Limited understanding of model decision-making processes

== Benefits ==
The benefits of this technology include:
* Improved accuracy in explaining model predictions
* Enhanced interpretability of machine learning models
* Better understanding of model behavior

== Potential Commercial Applications ==
This technology has potential commercial applications in:
* Financial services for risk assessment
* Healthcare for predictive modeling
* E-commerce for personalized recommendations

== Possible Prior Art ==
One possible prior art could be the use of k-nearest neighbors algorithms for generating interpretable examples in machine learning models.

=== Unanswered Questions ===

==== How does this technology compare to other post-hoc explanation methods? ====
This article does not provide a direct comparison with other post-hoc explanation methods in terms of accuracy, efficiency, or scalability.

==== What are the limitations of using a variational autoencoder for neighborhood sampling? ====
The article does not discuss any potential limitations or challenges associated with utilizing a variational autoencoder for neighborhood sampling.


==Original Abstract Submitted==

a computer-implemented method, system and computer program product for utilizing a variational autoencoder for neighborhood sampling. a variational autoencoder is trained to generate in-distribution neighborhood samples. upon training the variational autoencoder to generate in-distribution neighborhood samples, in-distribution neighborhood samples of an instance of a dataset in latent space that satisfy a distortion constraint are generated using the trained variational autoencoder. a set of interpretable examples for the in-distribution neighborhood samples are then generated using a k-nearest neighbors algorithm. such interpretable examples are then used to explain the black box model's predictions. as a result, the accuracy of the decision making ability of post-hoc local explanation methods is improved.



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