18353243. COMPUTER-READABLE RECORDING MEDIUM STORING SAMPLING PROGRAM, SAMPLING METHOD, AND INFORMATION PROCESSING APPARATUS simplified abstract (Fujitsu Limited)
Contents
- 1 COMPUTER-READABLE RECORDING MEDIUM STORING SAMPLING PROGRAM, SAMPLING METHOD, AND INFORMATION PROCESSING APPARATUS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 COMPUTER-READABLE RECORDING MEDIUM STORING SAMPLING PROGRAM, SAMPLING METHOD, AND INFORMATION PROCESSING APPARATUS - 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 Unanswered Questions
- 1.11 Original Abstract Submitted
COMPUTER-READABLE RECORDING MEDIUM STORING SAMPLING PROGRAM, SAMPLING METHOD, AND INFORMATION PROCESSING APPARATUS
Organization Name
Inventor(s)
COMPUTER-READABLE RECORDING MEDIUM STORING SAMPLING PROGRAM, SAMPLING METHOD, AND INFORMATION PROCESSING APPARATUS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18353243 titled 'COMPUTER-READABLE RECORDING MEDIUM STORING SAMPLING PROGRAM, SAMPLING METHOD, AND INFORMATION PROCESSING APPARATUS
Simplified Explanation
The patent application describes a computer program that executes a sampling process using a machine learning model to convert data from a latent space to a data space, determine acceptance of the data as a transition state in a Markov chain Monte Carlo method, and output the accepted data as a sample.
- Converting data from a latent space to a data space using a machine learning model with a predetermined transformation rule.
- Determining acceptance of the converted data as a transition state in a Markov chain Monte Carlo method based on an acceptance probability.
- Outputting the accepted data as a sample of the transition state.
Potential Applications
This technology could be applied in fields such as data analysis, pattern recognition, and artificial intelligence.
Problems Solved
This technology helps in efficiently generating samples from complex probability distributions, which is useful in various statistical and machine learning applications.
Benefits
The technology offers a more effective and accurate method for sampling data, leading to improved analysis and decision-making processes.
Potential Commercial Applications
"Enhancing Data Sampling Efficiency with Machine Learning Model" could find applications in industries such as finance, healthcare, and marketing for data analysis and predictive modeling.
Possible Prior Art
One possible prior art could be the use of Markov chain Monte Carlo methods in sampling processes in machine learning and statistics.
Unanswered Questions
How does the acceptance probability affect the efficiency of the sampling process?
The acceptance probability plays a crucial role in determining the quality and speed of the sampling process.
What are the limitations of using a machine learning model for data conversion in this context?
The use of a machine learning model may introduce biases or errors in the sampling process, which could impact the accuracy of the results.
Original Abstract Submitted
A non-transitory computer-readable recording medium stores a program for causing a computer to execute a sampling process including: converting first data in a latent space into second data in a data space by using a machine learning model that has the latent space transformable into an isometric space with same probability distribution as the data space according to a predetermined transformation rule; determining whether or not to accept the second data as a transition state in a Markov chain Monte Carlo method from an accepted first sample in the data space with an acceptance probability based on the transformation rule; and outputting the second data as a second sample of the transition state from the first sample when the second data is determined to be accepted.
