18 citations · 111 across the 34 of their papers we have counts for
10 papers · 1 filter
Chasing Fairness in Graphs: A GNN Architecture Perspective
Zhimeng Jiang, Xiaotian Han, Chao Fan +4
There has been significant progress in improving the performance of graph neural networks (GNNs) through enhancements in graph data, model architecture design, and training strateg…
TVE: Learning Meta-attribution for Transferable Vision Explainer
Guanchu Wang, Yu-Neng Chuang, Fan Yang +8
Explainable machine learning significantly improves the transparency of deep neural networks. However, existing work is constrained to explaining the behavior of individual model p…
Setting the Trap: Capturing and Defeating Backdoors in Pretrained Language Models through Honeypots
Ruixiang Tang, Jiayi Yuan, Yiming Li +3
In the field of natural language processing, the prevalent approach involves fine-tuning pretrained language models (PLMs) using local samples. Recent research has exposed the susc…
Efficient GNN Explanation via Learning Removal-based Attribution
Yao Rong, Guanchu Wang, Qizhang Feng +4
As Graph Neural Networks (GNNs) have been widely used in real-world applications, model explanations are required not only by users but also by legal regulations. However, simultan…
Editable Graph Neural Network for Node Classifications
Zirui Liu, Zhimeng Jiang, Shaochen Zhong +5
Despite Graph Neural Networks (GNNs) have achieved prominent success in many graph-based learning problem, such as credit risk assessment in financial networks and fake news detect…
Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models
Daochen Zha, Louis Feng, Liang Luo +8
Sharding a large machine learning model across multiple devices to balance the costs is important in distributed training. This is challenging because partitioning is NP-hard, and…
Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model
Zirui Liu, Guanchu Wang, Shaochen Zhong +8
With the rapid growth in model size, fine-tuning the large pre-trained language model has become increasingly difficult due to its extensive memory usage. Previous works usually fo…
Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt
Zhaozhuo Xu, Zirui Liu, Beidi Chen +5
While the numerous parameters in Large Language Models (LLMs) contribute to their superior performance, this massive scale makes them inefficient and memory-hungry. Thus, they are…
Efficient XAI Techniques: A Taxonomic Survey
Yu-Neng Chuang, Guanchu Wang, Fan Yang +4
Recently, there has been a growing demand for the deployment of Explainable Artificial Intelligence (XAI) algorithms in real-world applications. However, traditional XAI methods ty…
Retiring DP: New Distribution-Level Metrics for Demographic Parity
Xiaotian Han, Zhimeng Jiang, Hongye Jin +4
Demographic parity is the most widely recognized measure of group fairness in machine learning, which ensures equal treatment of different demographic groups. Numerous works aim to…