Randomly Weighted Neuromodulation in Neural Networks Facilitates Learning of Manifolds Common Across Tasks

• URL: http://arxiv.org/abs/2401.02437v1
• Date: Fri, 17 Nov 2023 15:22:59 GMT
• Title: Randomly Weighted Neuromodulation in Neural Networks Facilitates Learning of Manifolds Common Across Tasks
• Authors: Jinyung Hong, Theodore P. Pavlic
• Abstract summary: Geometric Sensitive Hashing functions are neural network models that learn class-specific manifold geometry in supervised learning. We show that a randomly weighted neural network with a neuromodulation system can realize this function.
• Score: 1.9580473532948401
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Geometric Sensitive Hashing functions, a family of Local Sensitive Hashing functions, are neural network models that learn class-specific manifold geometry in supervised learning. However, given a set of supervised learning tasks, understanding the manifold geometries that can represent each task and the kinds of relationships between the tasks based on them has received little attention. We explore a formalization of this question by considering a generative process where each task is associated with a high-dimensional manifold, which can be done in brain-like models with neuromodulatory systems. Following this formulation, we define \emph{Task-specific Geometric Sensitive Hashing~(T-GSH)} and show that a randomly weighted neural network with a neuromodulation system can realize this function.

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