Indexing Diverse Machine Learning Models via Hyperparameter Generalization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Managing and indexing diverse machine learned models is challenging due to differences in hyperparameters, leading to difficulties in maintenance, storage, and retrieval, as well as the inability to effectively index and utilize models when their creators leave or move to other projects.
Innovation Solution
A system that generalizes machine learned models into neural network architectures, maps and indexes them by hyperparameters, and clusters them using structural and hyperparameter similarities, enabling efficient searching and retrieval of similar models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learned models are indexed by their original hyperparameters, then each model can be precisely identified, but different types of models have different hyperparameter types that are not comparable, making unified indexing impossible
Solution Approach 1:
The patent transforms diverse model hyperparameters into a unified set of standardized parameters including model type, input features, output features, performance metrics, and resource requirements. This parameter transformation enables different model types (neural networks, decision trees, regression models) to be indexed and compared using common criteria while preserving their unique characteristics through the model type identifier.
2Loss of information
If models are stored with detailed hyperparameter information, then model retrieval can be precise, but the system complexity increases making maintenance difficult
Solution Approach 1:
The patent segments model information into distinct categories: essential identification parameters (model type, input/output features), performance parameters (accuracy, precision, recall), and resource parameters (computational cost, memory usage). This segmentation allows the system to manage and retrieve models efficiently by focusing on relevant parameter subsets rather than handling all hyperparameters uniformly.
Solution Approach 2:
The patent introduces standardized parameter schemas as intermediaries between diverse model hyperparameters and the indexing system. These schemas act as translation layers that convert model-specific hyperparameters into universal descriptors, simplifying the indexing process while preserving the original model characteristics through structured data representation.
3Ease of operation
If models are organized by their specific hyperparameters, then model-specific retrieval is possible, but cross-model comparison and clustering become difficult
Solution Approach 1:
The patent creates a universal parameter framework that serves multiple functions simultaneously: it enables precise model retrieval through standardized criteria, supports cross-model comparison by providing common comparison metrics, and facilitates model clustering through grouped parameter categories. This multi-functional parameter system handles diverse model types (classification, regression, clustering models) under a unified indexing approach.
Data Source
AI summary
The present disclosure relates to systems and methods for indexing and clustering machine learned models. Moreover, the present disclosure relates to systems and methods for searching indexed machine learned models and receiving suggested models based on the clustering of the same.