Neural Network Evaluation Using Trained Classifier
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Neural networks lack transparency in their decision-making processes, making it difficult for humans to understand why they produce specific outputs, which raises concerns about their reliability and performance.
Innovation Solution
A system and method for evaluating neural networks using a trained classifier that distinguishes between reliable and unreliable networks based on supervised training data, including feature vectors and labels, to assess their reliability and performance criteria such as accuracy, efficiency, and correctness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a neural network is used to solve complex problems, then problem-solving capability is improved, but transparency and understandability of decision-making deteriorates
Solution Approach 1:
The patent introduces an intermediary evaluation system that acts as a mediator between the neural network and human users. This evaluation system generates explanations and assessments of the neural network's decisions, making the previously opaque decision-making process transparent and understandable without modifying the neural network's core problem-solving capabilities.
2Reliability
If a neural network is trained with supervised training data, then accuracy and reliability are improved, but evaluation and verification of performance becomes more complex
Solution Approach 1:
The patent segments the evaluation process into distinct components: feature extraction from training data, classification of neural network behaviors, and generation of evaluation metrics. This segmentation simplifies the overall evaluation complexity by breaking down the verification task into manageable, modular steps that can be independently implemented and tested.
Data Source
AI summary
A computer system includes a memory storing a data structure representing a neural network. The data structure includes a plurality of fields including values representing topology of the neural network. The computer system also includes one or more processors configured to perform neural network classification by operations including generating a vector representing at least a portion of the neural network based on the data structure. The operations also include providing the vector as input to a trained classifier to generate a classification result associated with at least the portion of the neural network, where the classification result is indicative of expected performance or reliability of the neural network. The operations also include generating an output indicative of the classification result.