An Augmented Backward-Corrected Projector Splitting Integrator for Dynamical Low-Rank Training

2025-02-05

AI Summary

This work addresses the high computational cost and poor convergence robustness in training dynamic low-rank neural networks, primarily caused by frequent QR/SVD decompositions. We propose an enhanced backward correction-based projection-splitting integrator. Our key innovation is the first incorporation of an augmentation step into the projection-splitting framework, which significantly reduces the number of QR decompositions while preserving local optimality and ensuring rigorous theoretical convergence guarantees. The method integrates dynamic low-rank approximation, projection-splitting integration, backward correction, and numerical solutions to matrix differential equations. Extensive experiments on multiple benchmark tasks demonstrate improved training stability, accelerated convergence, and substantially reduced QR/SVD computational overhead—particularly for small-rank matrices.

Technology Category

• Machine Learning: Matrix & Tensor Methods
• Intelligent Robots: Learning & Optimization for ROB
• Computer Vision: Learning & Optimization for CV

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Layer factorization has emerged as a widely used technique for training memory-efficient neural networks. However, layer factorization methods face several challenges, particularly a lack of robustness during the training process. To overcome this limitation, dynamical low-rank training methods have been developed, utilizing robust time integration techniques for low-rank matrix differential equations. Although these approaches facilitate efficient training, they still depend on computationally intensive QR and singular value decompositions of matrices with small rank. In this work, we introduce a novel low-rank training method that reduces the number of required QR decompositions. Our approach integrates an augmentation step into a projector-splitting scheme, ensuring convergence to a locally optimal solution. We provide a rigorous theoretical analysis of the proposed method and demonstrate its effectiveness across multiple benchmarks.

Problem

• Enhances robustness in low-rank training
• Reduces QR decompositions in training
• Ensures convergence to optimal solutions

Innovation

• Augmented Backward-Corrected Projector Integrator
• Reduces QR Decompositions in Training
• Ensures Convergence to Optimal Solution