Walking the Weight Manifold: a Topological Approach to Conditioning Inspired by Neuromodulation

This research introduces a method inspired by neuromodulation, where neural network weights are smoothly parameterized as manifolds conditioned on task variables, enabling better generalization and task transfer. By optimizing these manifolds' topology, such as lines or circles, the approach outperforms traditional input conditioning methods and offers a flexible framework for multi-task learning.