Neuronal Fluctuations: Learning Rates vs Participating Neurons

TL;DR

This paper explores how different learning rates affect internal neural fluctuations in deep neural networks, revealing their impact on training dynamics and model performance to improve hyperparameter tuning strategies.

Contribution

It systematically analyzes the relationship between learning rates and neural parameter fluctuations, providing new insights into the optimization process in deep learning.

Findings

Higher learning rates increase fluctuation magnitude.

Fluctuation patterns correlate with final accuracy.

Optimal fluctuations balance exploration and exploitation.

Abstract

Deep Neural Networks (DNNs) rely on inherent fluctuations in their internal parameters (weights and biases) to effectively navigate the complex optimization landscape and achieve robust performance. While these fluctuations are recognized as crucial for escaping local minima and improving generalization, their precise relationship with fundamental hyperparameters remains underexplored. A significant knowledge gap exists concerning how the learning rate, a critical parameter governing the training process, directly influences the dynamics of these neural fluctuations. This study systematically investigates the impact of varying learning rates on the magnitude and character of weight and bias fluctuations within a neural network. We trained a model using distinct learning rates and analyzed the corresponding parameter fluctuations in conjunction with the network's final accuracy. Our findings aim to establish a clear link between the learning rate's value, the resulting fluctuation patterns, and overall model performance. By doing so, we provide deeper insights into the optimization process, shedding light on how the learning rate mediates the crucial exploration-exploitation trade-off during training. This work contributes to a more nuanced understanding of hyperparameter tuning and the underlying mechanics of deep learning.