Neural Model Parentage Mapping for Repository Reuse
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Solution Overview
Problem
The lack of structured representation and parentage information for neural networks in public and proprietary repositories hinders users' ability to navigate, reuse, and understand the heritage of machine learning models, leading to inefficiencies in resource usage and environmental impact.
Innovation Solution
A computer-implemented method and system that analyzes machine learning model weights to determine parent-child relationships and construct model trees and graphs, using distance measures and temporal order to predict model heritage, enabling structured visualization and metadata imputation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If structured representation and parentage information are added to neural networks in repositories, then model discovery and reuse efficiency improve, but system complexity and data processing requirements increase
Solution Approach 1:
The patent pre-computes and stores structured representations, parentage information, and metadata for models before users need them. This includes calculating distance measures between models in advance and organizing them in searchable formats, so when users query the repository, the information is already prepared and readily available, eliminating the need for complex real-time analysis
Solution Approach 2:
The patent introduces an intermediary indexing system that acts as a mediator between the raw model data and user queries. This intermediary layer pre-organizes models based on their parentage relationships, tasks, and performance metrics, allowing users to efficiently discover and reuse models without directly querying the entire model repository, thus reducing system complexity
2Measurement precision
If distance measures based on internal learned representations are calculated for all model pairs, then model parentage prediction accuracy improves, but computational resources and time increase
Solution Approach 1:
The patent segments the computational task of calculating distance measures by dividing the model repository into smaller groups or batches. Instead of calculating all pairwise distances simultaneously, the system processes models in segments, calculating distances for subsets of models independently. This reduces the memory footprint and allows parallel processing, significantly reducing computational time while maintaining accuracy
Solution Approach 2:
The patent applies partial action by calculating distance measures only for necessary model comparisons rather than all possible pairs. The system uses sampling strategies and selective computation based on query requirements, calculating precise distance measures only when needed for parentage prediction, rather than pre-computing all possible distances which would be excessively time-consuming
3Adaptability or versatility
If comprehensive model metadata and structured information are stored for all models, then model comparison and indexing capability improve, but storage requirements and data management complexity increase
Solution Approach 1:
The patent extracts only the essential and most relevant metadata and structured information from complete model datasets. Instead of storing all possible model attributes, the system identifies and stores only the critical features needed for parentage prediction, task classification, and performance comparison. This selective extraction reduces storage requirements while maintaining the versatility needed for effective model comparison and indexing
Data Source
AI summary
A computer-implemented method comprising: receiving, as input, a set of machine learning models associated with a repository of models, wherein a creation time for each of the models in the set with respect to the repository is known; determining a distance measure with respect to each pair of models in the set, based, at least in part, on a set of internal learned representations which determine how each of the models processes and encodes input data; and predicting, for each model m in the set, a parent model p from which the model m was generated via additional training, based, at least in part, on (x) the distance measure, and (y) temporal order and distance determined based on the creation time, between the model m and the parent model p.