Regularized Adaptive Momentum Dual Averaging Algorithm for Training Structured Neural Networks

Kernkonzepte

RAMDA is the first regularized adaptive method outputting models with outstanding predictive performance while being optimally structured.

Zusammenfassung

The article introduces the Regularized Adaptive Momentum Dual Averaging (RAMDA) algorithm for training structured neural networks. It proposes an inexactness condition and a subproblem solver to make regularized adaptive methods practically feasible. RAMDA guarantees locally optimal structures and outperforms existing methods in various tasks like computer vision, language modeling, and speech tasks.

1. Introduction
   • Challenge of large neural network models.
   • Importance of exploiting structures to reduce costs.
2. Regularized Adaptive Methods
   • Inducing desirable structures with regularizers.
   • Challenges in solving subproblems efficiently.
3. Manifold Identification
   • Theory behind identifying optimal structures.
4. Algorithm Description
   • RAMDA's approach to finding locally optimal structures.
5. Related Work
   • Comparison with existing regularized methods.
6. Subproblem Solver Efficiency
   • Effectiveness of the proposed subproblem solver.
7. Experiments
   • Results on image classification, language modeling, and speech synthesis tasks.
8. Conclusions
   • Superiority of RAMDA in achieving high structured sparsity and prediction performance.

Statistiken

We propose an implementable inexactness condition that retains convergence guarantees similar to exact versions.
Extensive experiments show RAMDA consistently outperforms state-of-the-art methods.

Zitate

"RAMDA is guaranteed to obtain the best structure possible among all methods converging to the same point."
"Extensive numerical experiments show that RAMDA is efficient and consistently outperforms state-of-the art."

Wichtige Erkenntnisse aus

Regularized Adaptive Momentum Dual Averaging with an Efficient Inexact Subproblem Solver for Training Structured Neural Network

by Zih-Syuan Hu... um arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.14398.pdf

Tiefere Fragen

How does RAMDA's adaptiveness contribute to its superior performance

RAMDA's adaptiveness contributes to its superior performance by allowing the algorithm to dynamically adjust its learning rate and momentum factor based on the characteristics of the optimization landscape. This adaptiveness enables RAMDA to navigate complex and non-convex spaces more effectively, leading to faster convergence and better generalization. By incorporating adaptiveness, RAMDA can efficiently handle varying gradients and noisy data, making it well-suited for training structured neural networks across different tasks.

What are the implications of the proposed inexactness condition on practical applications

The proposed inexactness condition has significant implications for practical applications in machine learning. By introducing an implementable measure of inexactness that ensures convergence guarantees are maintained while solving subproblems approximately, algorithms like RAMDA become more efficient and feasible for real-world use cases. This condition allows for a balance between computational efficiency and accuracy, enabling practitioners to apply regularized adaptive methods effectively even when closed-form solutions are not available.

How can manifold identification theory be further leveraged in machine learning research

Manifold identification theory can be further leveraged in machine learning research to enhance model interpretability, improve optimization strategies, and guide feature selection processes. By leveraging manifold identification techniques, researchers can identify locally optimal structures induced by regularizers at stationary points more effectively. This can lead to models with improved predictive performance while maintaining desirable structural properties. Additionally, manifold identification theory can help uncover hidden patterns within high-dimensional data spaces and facilitate the development of more robust machine learning algorithms with enhanced generalization capabilities.