85 citations · 204 across the 23 of their papers we have counts for
11 papers · 1 filter
MTL-LoRA: Low-Rank Adaptation for Multi-Task Learning
Yaming Yang, Dilxat Muhtar, Yelong Shen +9
Parameter-efficient fine-tuning (PEFT) has been widely employed for domain adaptation, with LoRA being one of the most prominent methods due to its simplicity and effectiveness. Ho…
You Can't Ignore Either: Unifying Structure and Feature Denoising for Robust Graph Learning
Tianmeng Yang, Jiahao Meng, Min Zhou +4
Recent research on the robustness of Graph Neural Networks (GNNs) under noises or attacks has attracted great attention due to its importance in real-world applications. Most previ…
Convolution-enhanced Evolving Attention Networks
Yujing Wang, Yaming Yang, Zhuo Li +7
Attention-based neural networks, such as Transformers, have become ubiquitous in numerous applications, including computer vision, natural language processing, and time-series anal…
Binary Classification with Positive Labeling Sources
Jieyu Zhang, Yujing Wang, Yaming Yang +2
To create a large amount of training labels for machine learning models effectively and efficiently, researchers have turned to Weak Supervision (WS), which uses programmatic label…
WRENCH: A Comprehensive Benchmark for Weak Supervision
Jieyu Zhang, Yue Yu, Yinghao Li +4
Recent Weak Supervision (WS) approaches have had widespread success in easing the bottleneck of labeling training data for machine learning by synthesizing labels from multiple pot…
Creating Training Sets via Weak Indirect Supervision
Jieyu Zhang, Bohan Wang, Xiangchen Song +4
Creating labeled training sets has become one of the major roadblocks in machine learning. To address this, recent \emph{Weak Supervision (WS)} frameworks synthesize training label…
Graph Pointer Neural Networks
Tianmeng Yang, Yujing Wang, Zhihan Yue +3
Graph Neural Networks (GNNs) have shown advantages in various graph-based applications. Most existing GNNs assume strong homophily of graph structure and apply permutation-invarian…
Evolving Attention with Residual Convolutions
Yujing Wang, Yaming Yang, Jiangang Bai +6
Transformer is a ubiquitous model for natural language processing and has attracted wide attentions in computer vision. The attention maps are indispensable for a transformer model…
How Does Supernet Help in Neural Architecture Search?
Yuge Zhang, Quanlu Zhang, Yaming Yang
Weight sharing, as an approach to speed up architecture performance estimation has received wide attention. Instead of training each architecture separately, weight sharing builds…
TextNAS: A Neural Architecture Search Space tailored for Text Representation
Yujing Wang, Yaming Yang, Yiren Chen +7
Learning text representation is crucial for text classification and other language related tasks. There are a diverse set of text representation networks in the literature, and how…
DeGNN: Characterizing and Improving Graph Neural Networks with Graph Decomposition
Xupeng Miao, Nezihe Merve Gürel, Wentao Zhang +17
Despite the wide application of Graph Convolutional Network (GCN), one major limitation is that it does not benefit from the increasing depth and suffers from the oversmoothing pro…