12 citations · 12 across the 2 of their papers we have counts for
2 papers
cs.LG2024
Reducing Fine-Tuning Memory Overhead by Approximate and Memory-Sharing Backpropagation
Yuchen Yang, Yingdong Shi, Cheems Wang +3
Fine-tuning pretrained large models to downstream tasks is an important problem, which however suffers from huge memory overhead due to large-scale parameters. This work strives to…
cs.LG2020★ 12 cited
Learning to Learn Kernels with Variational Random Features
Xiantong Zhen, Haoliang Sun, Yingjun Du +4
In this work, we introduce kernels with random Fourier features in the meta-learning framework to leverage their strong few-shot learning ability. We propose meta variational rando…