3 papers
cs.LG2025
Memory-Efficient Training with In-Place FFT Implementation
Xinyu Ding, Bangtian Liu, Siyu Liao +1
Fast Fourier Transforms (FFT) are widely used to reduce memory and computational costs in deep learning. However, existing implementations, including standard FFT and real FFT (rFF…
cs.CL2025
Block Circulant Adapter for Large Language Models
Xinyu Ding, Meiqi Wang, Siyu Liao +1
Fine-tuning large language models (LLMs) is difficult due to their huge model size. Recent Fourier domain-based methods show potential for reducing fine-tuning costs. We propose a…
cs.LG2025
Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors
Xinyu Ding, Lexuan Chen, Siyu Liao +1
Foundation models have achieved tremendous success in different domains. However, their huge computation and storage complexity make these models difficult to fine-tune and also le…