Reference Point Determination for Machine Learning Architectures
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Solution Overview
Problem
Existing machine learning techniques face challenges in determining representative reference points for physical system models, leading to over-parameterization and inefficient learning rates, especially in costly fields like medicine and research, where data acquisition is expensive and limited.
Innovation Solution
A machine learning development architecture that determines reference points using correlation metrics to optimize parameter selection, allowing for more feasible and effective simulation of physical systems, enabling efficient training of models for various applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional machine learning techniques are used for physical system models, then data acquisition can proceed, but over-parameterization occurs leading to infeasible parameter selection and poor learning rates
Solution Approach 1:
The patent extracts and removes redundant parameters from the parameter space by identifying and eliminating over-parameterization. This is achieved through systematic analysis of the physical system model to distinguish essential parameters from redundant ones, thereby simplifying the parameter space while maintaining the representativeness of reference points for machine learning training.
Solution Approach 2:
The patent transforms the parameter selection process by changing from traditional arbitrary or exhaustive parameter sampling to a systematic method that identifies optimal parameter values based on physical system characteristics. This involves redefining how parameters are selected and weighted to achieve better learning rates and feasibility while maintaining reliability.
2Measurement precision
If comprehensive parameter sampling is performed to ensure representativeness, then learning accuracy improves, but computational cost and data acquisition expenses increase
Solution Approach 1:
The patent applies partial action by selecting only the necessary subset of parameters and reference points needed for effective machine learning training, rather than performing exhaustive sampling of the entire parameter space. This selective approach achieves sufficient learning accuracy while significantly reducing the quantity of data acquisition required, thereby lowering costs in expensive fields like medicine and research.
Solution Approach 2:
The patent performs preliminary analysis of the physical system model before data acquisition to identify the most critical parameters and optimal reference points. This preliminary action enables the formulation of an efficient sampling strategy in advance, ensuring that subsequent data acquisition focuses only on the most informative parameters, thus improving learning accuracy while minimizing data acquisition costs.
3Productivity
If traditional parameter selection methods are used, then implementation is simple, but learning rates remain low and model training is inefficient
Solution Approach 1:
The patent introduces an intermediary systematic framework that bridges simple parameter selection and high learning rates. This framework acts as a mediator by providing structured guidelines and methods for parameter selection that are more sophisticated than traditional approaches yet remain implementable. The intermediary structure enables efficient model training by organizing parameter selection in a systematic way that improves learning rates without excessive complexity.
Data Source
AI summary
This disclosure relates to improved techniques for determining reference points for computerized simulations of physical systems and/or physical models that may be used in machine learning development architectures. This disclosure also relates to systems, methods, apparatuses, and computer program products that are configured to determine reference points for one or more parameters of a model of a physical system used in a computerized simulation of the model. The reference points may be representative of the system outputs across the parameter space, and can be determined in an efficient and computationally-feasible manner. The outputs of the computerized simulations of physical systems may then be further used to create, build, or train one or more learning models pertaining to physical systems.