Core Concepts

The authors investigate the impact of momentum on optimization trajectories, revealing a unique quantity that defines the path and acceleration rule. They characterize the recovered solution through implicit regularization, showing how small values of a specific parameter help recover sparse solutions.

Abstract

In this work, the authors delve into the effect of momentum on optimization paths in neural network training. They explore how momentum influences generalization performance and reveal insights into overparametrized linear regression. The study highlights the importance of balancedness and asymptotic balancedness in determining the recovered solution's properties. By analyzing continuous-time approaches, they provide valuable insights into understanding momentum's role in training diagonal linear networks.

Stats

λ = 0.2
λ = 0.5
λ = 2
λ = 8

Quotes

"Momentum gradient flow recovers solutions which generalize better than those selected by gradient flow."
"The trajectory of MGD is solely determined by a single parameter intertwining step size and momentum."
"Initializations with entries of different magnitudes can hinder the recovery of a sparse vector."

Key Insights Distilled From

by Hristo Papaz... at **arxiv.org** 03-11-2024

Deeper Inquiries

Momentum impacts convergence speed by accelerating the optimization process compared to gradient descent. In momentum methods, such as momentum gradient descent (MGD), a parameter called λ is introduced which uniquely defines the optimization path. This parameter depends on the step size γ and momentum parameter β, intertwining their roles in influencing the trajectory of MGD. Small values of λ lead to faster convergence speeds, allowing for quicker optimization towards the minimum of the loss function. Additionally, through an acceleration rule derived from Corollary 1, it is shown that adjusting parameters like γ can further accelerate convergence without changing the trajectory significantly.

Initializations with varying magnitudes can have implications on solution recovery in neural network training scenarios. When considering small initialisation scales α, solutions recovered by gradient flow tend to exhibit a sparse nature due to implicit regularization effects associated with hyperbolic entropy functions at scale ∆0. However, if initializations have entries with different magnitudes leading to non-homogeneous balancedness ∆0 across weights u and v in a diagonal linear network setting, this could hinder sparse recovery efforts during training.

Balancedness plays a crucial role in determining generalization properties beyond neural network training when considering models trained using techniques like gradient flow or momentum gradient flow (MGF). The asymptotic balancedness (∆∞) characterizes how well-trained models interpolate datasets while also providing insights into sparsity levels within recovered solutions. A non-zero balancedness ensures that solutions converge towards interpolators defined by constrained minimization problems involving hyperbolic entropy functions scaled at ∆∞. This balance between feature learning and kernel regimes influences model performance and generalization capabilities post-training.

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