1 code implementation • 23 Mar 2024 • Jiacheng Ruan, Jingsheng Gao, Mingye Xie, Daize Dong, Suncheng Xiang, Ting Liu, Yuzhuo Fu
Adapter-Tuning (AT) method involves freezing a pre-trained model and introducing trainable adapter modules to acquire downstream knowledge, thereby calibrating the model for better adaptation to downstream tasks.
1 code implementation • 4 Feb 2024 • Zhangyang Gao, Daize Dong, Cheng Tan, Jun Xia, Bozhen Hu, Stan Z. Li
(4) The edge-centric pretraining framework GraphsGPT demonstrates its efficacy in graph domain tasks, excelling in both representation and generation.
1 code implementation • 10 Nov 2022 • Shwai He, Liang Ding, Daize Dong, Boan Liu, Fuqiang Yu, DaCheng Tao
The main contributions of our work are challenging the basic commonsense in dynamic networks and proposing a partially dynamic network, namely PAD-Net, to transform the redundant dynamic parameters into static ones.
1 code implementation • 9 Oct 2022 • Shwai He, Liang Ding, Daize Dong, Miao Zhang, DaCheng Tao
Adapter Tuning, which freezes the pretrained language models (PLMs) and only fine-tunes a few extra modules, becomes an appealing efficient alternative to the full model fine-tuning.
no code implementations • 5 Apr 2022 • Shwai He, Chenbo Jiang, Daize Dong, Liang Ding
Dynamic convolution achieves better performance for efficient CNNs at the cost of negligible FLOPs increase.