Graph-Based Model Topology Optimization via Neural Network
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Solution Overview
Problem
Existing graph-based models often have inefficient topologies due to oversized structures, leading to increased computational overhead and decreased performance, which limits their effectiveness in achieving specific objectives in fields like computer vision, natural language processing, and drug design.
Innovation Solution
A method that encodes a graph-based model into a neural network topology optimizer format, allowing for topological mutations such as adding or removing connections and nodes, and tuning parameters using training data to optimize the model's performance, with validation to select the most improved entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the graph-based model uses a comprehensive topology structure to ensure coverage of all data patterns, then the model's ability to learn from experience is improved, but the computational overhead increases and performance decreases
Solution Approach 1:
The patent segments the graph-based model topology into multiple entities or sub-structures that can be independently optimized. Each entity represents a portion of the overall model topology, allowing selective mutation and optimization of specific segments rather than the entire model, thus reducing computational overhead while maintaining learning effectiveness.
Solution Approach 2:
The patent implements dynamic topology optimization by allowing the model structure to evolve and adapt during the learning process. The system dynamically creates mutant entities with modified topologies, evaluates their performance, and selectively integrates improvements, enabling the model to find an optimal balance between comprehensive coverage and computational efficiency.
2Reliability
If the graph-based model uses a detailed topology structure to capture complex patterns, then the model's learning capability is improved, but the model complexity increases
Solution Approach 1:
The patent applies local quality by allowing different portions of the model topology to have different levels of complexity and mutation rates. High-importance regions may maintain detailed structures for accurate pattern recognition, while less critical regions can be simplified, optimizing the overall model complexity while preserving essential learning capabilities.
Solution Approach 2:
The patent changes topological parameters such as connection densities, node degrees, and structural configurations to optimize the model. By systematically varying these parameters and evaluating performance, the system identifies optimal complexity levels that capture complex patterns without unnecessary model complexity.
3Stability of the object's composition
If the model topology is fixed to maintain stability, then the model's reliability is improved, but the ability to adapt and optimize performance is reduced
Solution Approach 1:
The patent performs preliminary actions by creating a pool of mutant entities with pre-computed topological variations before full evaluation. This allows the system to prepare multiple candidate structures in advance, maintaining stability during the base model operation while having pre-computed alternatives ready for adaptation when performance improvement is needed.
Solution Approach 2:
The patent implements feedback mechanisms where the performance of mutant entities is evaluated against the original model, and only improvements are integrated back into the main model. This feedback loop maintains stability by rejecting detrimental changes while enabling adaptation through selective incorporation of beneficial topological mutations.
Data Source
AI summary
A method may include receiving a graph-based model in a first format, including a static topology of the graph-based model. The method may also include encoding the graph-based model from the first format into a neural network topology optimizer (NNTO) readable format such that the topology of the encoded graph-based model is configured to be altered; creating a first group of entities based on at least a same portion of the encoded graph-based model; and performing a learning operation by tuning parameters of the first group of entities to produce an optimization score for each entity. Additionally, the method may include performing a validation operation; determining that an improvement in validation performance for at least one entity is within a threshold amount of improvement; selecting a solution entity; and adding the selected solution entity into the graph-based model in place of the same portion.