Publications (7)
Do Protein Transformers Have Biological Intelligence?
Fudong Lin, Wanrou Du, Jinchan Liu +5
Deep neural networks, particularly Transformers, have been widely adopted for predicting the functional properties of proteins. In this work, we focus on exploring whether Protein…
Towards Interpretable Adversarial Examples via Sparse Adversarial Attack
Fudong Lin, Jiadong Lou, Hao Wang +2
Sparse attacks are to optimize the magnitude of adversarial perturbations for fooling deep neural networks (DNNs) involving only a few perturbed pixels (i.e., under the l0 constrai…
MMST-ViT: Climate Change-aware Crop Yield Prediction via Multi-Modal Spatial-Temporal Vision Transformer
Fudong Lin, Summer Crawford, Kaleb Guillot +14
Precise crop yield prediction provides valuable information for agricultural planning and decision-making processes. However, timely predicting crop yields remains challenging as c…
ECGN: A Cluster-Aware Approach to Graph Neural Networks for Imbalanced Classification
Bishal Thapaliya, Anh Nguyen, Yao Lu +7
Classifying nodes in a graph is a common problem. The ideal classifier must adapt to any imbalances in the class distribution. It must also use information in the clustering struct…
Towards Robust Vision Transformer via Masked Adaptive Ensemble
Fudong Lin, Jiadong Lou, Xu Yuan +1
Adversarial training (AT) can help improve the robustness of Vision Transformers (ViT) against adversarial attacks by intentionally injecting adversarial examples into the training…
Long-Tailed Recognition via Information-Preservable Two-Stage Learning
Fudong Lin, Xu Yuan
The imbalance (or long-tail) is the nature of many real-world data distributions, which often induces the undesirable bias of deep classification models toward frequent classes, re…
An Open and Large-Scale Dataset for Multi-Modal Climate Change-aware Crop Yield Predictions
Fudong Lin, Kaleb Guillot, Summer Crawford +3
Precise crop yield predictions are of national importance for ensuring food security and sustainable agricultural practices. While AI-for-science approaches have exhibited promisin…