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

international business machines corporation

Inventor(s)

Natalia Martinez Gil of Durham NC (US)

Kanthi Sarpatwar of Elmsford NY (US)

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.