Neural Network Pipeline for Abstract Reasoning
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Solution Overview
Problem
Current methods for solving cognitive tasks that require learning abstract properties, such as visual oddity tasks, face challenges in effectively capturing relational properties of input datasets and achieving high accuracy, particularly in tasks involving complex geometrical concepts.
Innovation Solution
A computer-implemented method using a pipeline of neural networks, where the first neural network generates embeddings from input datasets, pairs of embeddings are formed and input into a second neural network to capture relational properties, and a third neural network produces output values that allow for the learning of abstract properties, with regularization using a mutual information-based regularizer to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single neural network is used to solve cognitive tasks requiring abstract property learning, then the system is simpler, but the accuracy and ability to capture relational properties are insufficient
Solution Approach 1:
The neural network system is divided into three distinct networks: a first neural network for generating initial embeddings from input datasets, a second neural network for capturing relational properties of embedding pairs, and a third neural network for producing final output values. This segmentation allows each network to specialize in a specific aspect of the cognitive task, thereby improving overall accuracy in capturing relational properties while maintaining manageable complexity through modular design.
2Reliability
If mutual information-based regularization is applied to the neural networks, then the test accuracy and performance are significantly improved, but the computational complexity and training time increase
Solution Approach 1:
A mutual information-based regularizer is introduced that computes the mutual information between embeddings and provides feedback during the training process. This feedback mechanism guides the neural networks to learn more informative representations by maximizing the mutual information between relevant variables, thereby stabilizing test accuracy and improving reliability. The regularizer continuously monitors and adjusts the learning process based on information-theoretic measures.
3Measurement precision
If pairs of embeddings are formed and processed through multiple neural networks, then the relational properties are captured more effectively, but the computational resources and processing time increase
Solution Approach 1:
The system extracts and processes only the essential relational information from input datasets by forming pairs of embeddings and feeding them through specialized neural networks. Rather than processing all possible combinations or raw data, the method extracts relevant embedding pairs that capture the essential relational properties, thereby reducing unnecessary computational energy consumption while maintaining high accuracy in relational property capture.
Data Source
AI summary
A computer-implemented method, system, and computer program product to solve a cognitive task that includes learning abstract properties. One embodiment may comprise accessing datasets that characterize the abstract properties. The accessed datasets may then be inputted into a first neural network to generate first embeddings. Pairs of the first embeddings generated may be formed, which correspond to pairs of the datasets. Data corresponding to the pairs formed may then be inputted into a second neural network, which may be executed to generate second embeddings. The latter may capture relational properties of the pairs of the datasets. A third neural network may be subsequently executed, based on the second embeddings generated, to obtain output values. One or more abstract properties of the datasets are learned based on the output values obtained, in order to solve the cognitive task.