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[2406.04112] Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation
Summary
This paper explores compressible dynamics in deep overparameterized low-rank learning, presenting methods to enhance training efficiency while retaining model benefits.
Why It Matters
As machine learning models grow in size, they demand more computational resources. This research offers a solution by leveraging low-dimensional structures, making it possible to maintain the advantages of overparameterization without the associated computational costs. This is particularly relevant for applications in deep learning and natural language processing.
Key Takeaways
- The paper introduces a method to utilize low-dimensional structures in deep learning models.
- It presents 'Deep LoRA', an improved technique for low-rank adaptation, enhancing efficiency and reducing overfitting.
- The approach demonstrates substantial improvements in training efficiency for deep matrix completion tasks.
- The findings are validated through experiments on natural language tasks with limited data.
- The research provides theoretical insights into the learning dynamics of overparameterized models.
Computer Science > Machine Learning arXiv:2406.04112 (cs) [Submitted on 6 Jun 2024 (v1), last revised 13 Feb 2026 (this version, v3)] Title:Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation Authors:Can Yaras, Peng Wang, Laura Balzano, Qing Qu View a PDF of the paper titled Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation, by Can Yaras and 3 other authors View PDF HTML (experimental) Abstract:While overparameterization in machine learning models offers great benefits in terms of optimization and generalization, it also leads to increased computational requirements as model sizes grow. In this work, we show that by leveraging the inherent low-dimensional structures of data and compressible dynamics within the model parameters, we can reap the benefits of overparameterization without the computational burdens. In practice, we demonstrate the effectiveness of this approach for deep low-rank matrix completion as well as fine-tuning language models. Our approach is grounded in theoretical findings for deep overparameterized low-rank matrix recovery, where we show that the learning dynamics of each weight matrix are confined to an invariant low-dimensional subspace. Consequently, we can construct and train compact, highly compressed factorizations possessing the same benefits as their overparameterized counterparts. In the context of deep matrix completion, our technique substantially improves training efficiency while re...
