Sine Activated Low-Rank Matrices for Parameter Efficient Learning

• URL: http://arxiv.org/abs/2403.19243v1
• Date: Thu, 28 Mar 2024 08:58:20 GMT
• Title: Sine Activated Low-Rank Matrices for Parameter Efficient Learning
• Authors: Yiping Ji, Hemanth Saratchandran, Cameron Gordon, Zeyu Zhang, Simon Lucey,
• Abstract summary: We propose a novel theoretical framework that integrates a sinusoidal function within the low-rank decomposition process. Our method proves to be an enhancement for existing low-rank models, as evidenced by its successful application in Vision Transformers (ViT), Large Language Models (LLMs), Neural Radiance Fields (NeRF)
• Score: 25.12262017296922
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-rank decomposition has emerged as a vital tool for enhancing parameter efficiency in neural network architectures, gaining traction across diverse applications in machine learning. These techniques significantly lower the number of parameters, striking a balance between compactness and performance. However, a common challenge has been the compromise between parameter efficiency and the accuracy of the model, where reduced parameters often lead to diminished accuracy compared to their full-rank counterparts. In this work, we propose a novel theoretical framework that integrates a sinusoidal function within the low-rank decomposition process. This approach not only preserves the benefits of the parameter efficiency characteristic of low-rank methods but also increases the decomposition's rank, thereby enhancing model accuracy. Our method proves to be an adaptable enhancement for existing low-rank models, as evidenced by its successful application in Vision Transformers (ViT), Large Language Models (LLMs), Neural Radiance Fields (NeRF), and 3D shape modeling. This demonstrates the wide-ranging potential and efficiency of our proposed technique.

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