Extrapolatable Relational Reasoning With Comparators in Low-Dimensional Manifolds
This addresses the issue of limited generalization in complex AI tasks like relational reasoning, which is crucial for building more robust and adaptable AI systems, though it appears incremental as it builds on existing neural network architectures.
The paper tackles the problem of poor out-of-distribution generalization in deep neural networks for relational reasoning tasks by introducing a neuroscience-inspired inductive bias module that projects high-dimensional object representations to low-dimensional manifolds, resulting in considerably better generalization performance across a range of tasks.
While modern deep neural architectures generalise well when test data is sampled from the same distribution as training data, they fail badly for cases when the test data distribution differs from the training distribution even along a few dimensions. This lack of out-of-distribution generalisation is increasingly manifested when the tasks become more abstract and complex, such as in relational reasoning. In this paper we propose a neuroscience-inspired inductive-biased module that can be readily amalgamated with current neural network architectures to improve out-of-distribution (o.o.d) generalisation performance on relational reasoning tasks. This module learns to project high-dimensional object representations to low-dimensional manifolds for more efficient and generalisable relational comparisons. We show that neural nets with this inductive bias achieve considerably better o.o.d generalisation performance for a range of relational reasoning tasks. We finally analyse the proposed inductive bias module to understand the importance of lower dimension projection, and propose an augmentation to the algorithmic alignment theory to better measure algorithmic alignment with generalisation.