International business machines corporation (20240104354). VARIATIONAL METHOD OF MAXIMIZING CONDITIONAL EVIDENCE FOR LATENT VARIABLE MODELS simplified abstract
Contents
- 1 VARIATIONAL METHOD OF MAXIMIZING CONDITIONAL EVIDENCE FOR LATENT VARIABLE MODELS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 VARIATIONAL METHOD OF MAXIMIZING CONDITIONAL EVIDENCE FOR LATENT VARIABLE MODELS - 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
VARIATIONAL METHOD OF MAXIMIZING CONDITIONAL EVIDENCE FOR LATENT VARIABLE MODELS
Organization Name
international business machines corporation
Inventor(s)
Takayuki Katsuki of Tokyo (JP)
VARIATIONAL METHOD OF MAXIMIZING CONDITIONAL EVIDENCE FOR LATENT VARIABLE MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240104354 titled 'VARIATIONAL METHOD OF MAXIMIZING CONDITIONAL EVIDENCE FOR LATENT VARIABLE MODELS
Simplified Explanation
The computer-implemented method described in the abstract is for learning with incomplete data, specifically focusing on maximizing a stochastically approximated conditional evidence lower bound to learn an unknown parameter in the predictive distribution of an outcome.
- Acquiring an incomplete set of covariates and an incomplete pattern indicating missing entries.
- Obtaining a predictive distribution of an outcome using the incomplete set of covariates and an unknown parameter.
- Learning the unknown parameter by maximizing a stochastically approximated conditional evidence lower bound.
- Controlling a density ratio by transforming parameters to keep the gradient below a threshold during maximization.
Potential Applications
This technology could be applied in fields such as machine learning, data analysis, and predictive modeling where incomplete data is common.
Problems Solved
This technology addresses the challenge of learning from incomplete data sets, improving the accuracy and reliability of predictive models in the presence of missing information.
Benefits
The method allows for more robust learning and prediction in scenarios where data may be incomplete, leading to more accurate outcomes and insights.
Potential Commercial Applications
Potential commercial applications include data analytics software, predictive modeling tools, and machine learning platforms for various industries.
Possible Prior Art
One possible prior art for this technology could be methods for handling missing data in statistical analysis, such as imputation techniques and Bayesian approaches.
Unanswered Questions
How does this method compare to existing techniques for learning with incomplete data?
This article does not provide a direct comparison to existing techniques for learning with incomplete data. Further research or a comparative study would be needed to evaluate the effectiveness and efficiency of this method compared to other approaches.
What are the limitations or constraints of this method in real-world applications?
The article does not discuss the limitations or constraints of implementing this method in real-world applications. It would be important to consider factors such as computational complexity, scalability, and generalizability when applying this method to practical scenarios.
Original Abstract Submitted
a computer-implemented method is provided for learning with incomplete data in which some of entries are missing. the method includes acquiring an incomplete set of covariates including incomplete features {tilde over (x)} and an incomplete pattern m indicating missing entries of the incomplete set of covariates {tilde over (x)}. the method further includes obtaining, by a hardware processor, a predictive distribution p(y|) of an outcome y by using the incomplete set of covariates and a parameter �, the parameter � being unknown. a learning of the parameter � includes performing a maximization by maximizing a stochastically approximated conditional evidence lower bound. the stochastically approximated conditional evidence lower bound includes a density ratio which is controlled by transforming a portion of parameters of the stochastically approximated conditional evidence lower bound to keep a gradient of the stochastically approximated conditional evidence lower bound below a threshold during the maximization.
