9 citations · 16 across the 11 of their papers we have counts for
11 papers
Corrupted but Not Broken: Understanding and Mitigating the Negative Impacts of Corrupted Data in Visual Instruction Tuning
Yunhao Gou, Hansi Yang, Zhili Liu +8
Visual Instruction Tuning (VIT) aims to enhance Multimodal Large Language Models (MLLMs), yet its effectiveness is often compromised by corrupted datasets with issues such as hallu…
Communication-Efficient and Privacy-Preserving Decentralized Meta-Learning
Hansi Yang, James T. Kwok
Distributed learning, which does not require gathering training data in a central location, has become increasingly important in the big-data era. In particular, random-walk-based…
Mixup Augmentation with Multiple Interpolations
Lifeng Shen, Jincheng Yu, Hansi Yang +1
Mixup and its variants form a popular class of data augmentation techniques.Using a random sample pair, it generates a new sample by linear interpolation of the inputs and labels.…
Loss-aware Curriculum Learning for Heterogeneous Graph Neural Networks
Zhen Hao Wong, Hansi Yang, Xiaoyi Fu +1
Heterogeneous Graph Neural Networks (HGNNs) are a class of deep learning models designed specifically for heterogeneous graphs, which are graphs that contain different types of nod…
Positive-Unlabeled Node Classification with Structure-aware Graph Learning
Hansi Yang, Yongqi Zhang, Quanming Yao +1
Node classification on graphs is an important research problem with many applications. Real-world graph data sets may not be balanced and accurate as assumed by most existing works…
An Adaptive Policy to Employ Sharpness-Aware Minimization
Weisen Jiang, Hansi Yang, Yu Zhang +1
Sharpness-aware minimization (SAM), which searches for flat minima by min-max optimization, has been shown to be useful in improving model generalization. However, since each SAM u…
Leveraging per Image-Token Consistency for Vision-Language Pre-training
Yunhao Gou, Tom Ko, Hansi Yang +3
Most existing vision-language pre-training (VLP) approaches adopt cross-modal masked language modeling (CMLM) to learn vision-language associations. However, we find that CMLM is i…
AutoWeird: Weird Translational Scoring Function Identified by Random Search
Hansi Yang, Yongqi Zhang, Quanming Yao
Scoring function (SF) measures the plausibility of triplets in knowledge graphs. Different scoring functions can lead to huge differences in link prediction performances on differe…
Topology-aware Tensor Decomposition for Meta-graph Learning
Hansi Yang, Peiyu Zhang, Quanming Yao
Heterogeneous graphs generally refers to graphs with different types of nodes and edges. A common approach for extracting useful information from heterogeneous graphs is to use met…
Searching to Exploit Memorization Effect in Learning from Corrupted Labels
Quanming Yao, Hansi Yang, Bo Han +2
Sample selection approaches are popular in robust learning from noisy labels. However, how to properly control the selection process so that deep networks can benefit from the memo…
Efficient Low-Rank Semidefinite Programming with Robust Loss Functions
Quanming Yao, Hangsi Yang, En-Liang Hu +1
In real-world applications, it is important for machine learning algorithms to be robust against data outliers or corruptions. In this paper, we focus on improving the robustness o…