Implicit Regularization in Feedback Alignment Learning Mechanisms for Neural Networks
This work addresses the need for better interpretability and performance in biologically inspired learning rules for distributed and privacy-aware machine learning, though it appears incremental in advancing existing FA methods.
The study tackled the problem of limited theoretical understanding and performance in multi-class classification for Feedback Alignment (FA) methods in neural networks, by introducing a unified framework that elucidates alignment principles, resulting in improved interpretability and groundwork for enhanced algorithms.
Feedback Alignment (FA) methods are biologically inspired local learning rules for training neural networks with reduced communication between layers. While FA has potential applications in distributed and privacy-aware ML, limitations in multi-class classification and lack of theoretical understanding of the alignment mechanism have constrained its impact. This study introduces a unified framework elucidating the operational principles behind alignment in FA. Our key contributions include: (1) a novel conservation law linking changes in synaptic weights to implicit regularization that maintains alignment with the gradient, with support from experiments, (2) sufficient conditions for convergence based on the concept of alignment dominance, and (3) empirical analysis showing better alignment can enhance FA performance on complex multi-class tasks. Overall, these theoretical and practical advancements improve interpretability of bio-plausible learning rules and provide groundwork for developing enhanced FA algorithms.