7 citations · 10 across the 8 of their papers we have counts for
8 papers
Visual prompting reimagined: The power of the Activation Prompts
Yihua Zhang, Hongkang Li, Yuguang Yao +5
Visual prompting (VP) has emerged as a popular method to repurpose pretrained vision models for adaptation to downstream tasks. Unlike conventional model fine-tuning techniques, VP…
Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis
Hongkang Li, Meng Wang, Shuai Zhang +2
Efficient training and inference algorithms, such as low-rank adaption and model pruning, have shown impressive performance for learning Transformer-based large foundation models.…
What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding
Hongkang Li, Meng Wang, Tengfei Ma +3
Graph Transformers, which incorporate self-attention and positional encoding, have recently emerged as a powerful architecture for various graph learning tasks. Despite their impre…
PSBD: Prediction Shift Uncertainty Unlocks Backdoor Detection
Wei Li, Pin-Yu Chen, Sijia Liu +1
Deep neural networks are susceptible to backdoor attacks, where adversaries manipulate model predictions by inserting malicious samples into the training data. Currently, there is…
How does promoting the minority fraction affect generalization? A theoretical study of the one-hidden-layer neural network on group imbalance
Hongkang Li, Shuai Zhang, Yihua Zhang +3
Group imbalance has been a known problem in empirical risk minimization (ERM), where the achieved high average accuracy is accompanied by low accuracy in a minority group. Despite…
Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark
Yihua Zhang, Pingzhi Li, Junyuan Hong +10
In the evolving landscape of natural language processing (NLP), fine-tuning pre-trained Large Language Models (LLMs) with first-order (FO) optimizers like SGD and Adam has become s…
On the Convergence and Sample Complexity Analysis of Deep Q-Networks with -Greedy Exploration
Shuai Zhang, Hongkang Li, Meng Wang +6
This paper provides a theoretical understanding of Deep Q-Network (DQN) with the -greedy exploration in deep reinforcement learning. Despite the tremendous empirical a…
AutoVP: An Automated Visual Prompting Framework and Benchmark
Hsi-Ai Tsao, Lei Hsiung, Pin-Yu Chen +2
Visual prompting (VP) is an emerging parameter-efficient fine-tuning approach to adapting pre-trained vision models to solve various downstream image-classification tasks. However,…