Search Results for author:
Found 5 papers, 3 papers with code
iDAT: inverse Distillation Adapter-Tuning
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.
A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer
Despite recent GNN and Graphformer efforts encoding graphs as Euclidean vectors, recovering original graph from the vectors remains a challenge.
PAD-Net: An Efficient Framework for Dynamic Networks
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.
SparseAdapter: An Easy Approach for Improving the Parameter-Efficiency of Adapters
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.
SD-Conv: Towards the Parameter-Efficiency of Dynamic Convolution
Dynamic convolution achieves better performance for efficient CNNs at the cost of negligible FLOPs increase.
