18167381. HIGH DIMENSIONAL SURROGATE MODELING FOR LEARNING UNCERTAINTY simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)
Contents
- 1 HIGH DIMENSIONAL SURROGATE MODELING FOR LEARNING UNCERTAINTY
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 HIGH DIMENSIONAL SURROGATE MODELING FOR LEARNING UNCERTAINTY - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
HIGH DIMENSIONAL SURROGATE MODELING FOR LEARNING UNCERTAINTY
Organization Name
INTERNATIONAL BUSINESS MACHINES CORPORATION
Inventor(s)
Shashanka Ubaru of Ossining NY (US)
Paz Fink Shustin of Tel-Aviv (IL)
Lior Horesh of North Salem NY (US)
Vasileios Kalantzis of White Plains NY (US)
HIGH DIMENSIONAL SURROGATE MODELING FOR LEARNING UNCERTAINTY - A simplified explanation of the abstract
This abstract first appeared for US patent application 18167381 titled 'HIGH DIMENSIONAL SURROGATE MODELING FOR LEARNING UNCERTAINTY
Simplified Explanation
The method described in the patent application involves using a variational autoencoder (VAE) to learn a low dimensional latent space representation of high dimensional data, and then using a polynomial chaos expansion to map new data samples in the latent space to the corresponding data output. This allows for estimation with high-dimensional datasets under uncertainty, such as missing values, by estimating the values using the set of distributions learned by the VAE.
- Variational autoencoder (VAE) used to learn low dimensional latent space representation of high dimensional data
- Encoder part of VAE outputs set of distributions of high dimensional dataset in latent space
- New data samples in latent space sampled using set of distributions from encoder part of VAE
- Polynomial chaos expansion used to map new data samples in latent space to corresponding data output
- Estimation with high-dimensional datasets under uncertainty, such as missing values, performed using set of distributions learned by VAE
Potential Applications
This technology could be applied in fields such as finance, healthcare, and manufacturing where high-dimensional datasets with missing values need to be analyzed and estimated.
Problems Solved
This technology solves the problem of estimating values in high-dimensional datasets with uncertainty, such as missing values, by learning a set of distributions in a latent space and mapping new data samples to the corresponding output.
Benefits
The benefits of this technology include improved estimation accuracy for high-dimensional datasets, the ability to handle missing values, and a more efficient way to analyze complex data.
Potential Commercial Applications
A potential commercial application of this technology could be in the development of software tools for data analysis and estimation in industries that deal with high-dimensional datasets.
Possible Prior Art
One possible prior art for this technology could be the use of variational autoencoders in machine learning for dimensionality reduction and data representation learning.
Original Abstract Submitted
A method to determine data uncertainty is provided. The method receives a high dimensional data input and a corresponding data output. The method trains a variational autoencoder (VAE) with the high dimensional data input to learn a low dimensional latent space representation of the high dimensional data input. An encoder part of the VAE outputs a set of distributions of the high dimensional dataset in a latent space. The method samples new data samples in the latent space using the set of distributions outputs from the encoder part of the VAE. The method learns a polynomial chaos expansion to map the new data samples in the latent space to the corresponding data output to learn the set of distributions and their relation to perform estimation with high-dimensional dataset under uncertainty such as missing values by estimating the values using the set of distributions.
