28 citations · 45 across the 12 of their papers we have counts for
12 papers
You Only Fine-tune Once: Many-Shot In-Context Fine-Tuning for Large Language Models
Wenchong He, Liqian Peng, Zhe Jiang +1
Large language models (LLMs) possess a remarkable ability to perform in-context learning (ICL), which enables them to handle multiple downstream tasks simultaneously without requir…
Accelerate Coastal Ocean Circulation Model with AI Surrogate
Zelin Xu, Jie Ren, Yupu Zhang +8
Nearly 900 million people live in low-lying coastal zones around the world and bear the brunt of impacts from more frequent and severe hurricanes and storm surges. Oceanographers s…
XTSFormer: Cross-Temporal-Scale Transformer for Irregular-Time Event Prediction in Clinical Applications
Tingsong Xiao, Zelin Xu, Wenchong He +8
Adverse clinical events related to unsafe care are among the top ten causes of death in the U.S. Accurate modeling and prediction of clinical events from electronic health records…
Spatial Knowledge-Infused Hierarchical Learning: An Application in Flood Mapping on Earth Imagery
Zelin Xu, Tingsong Xiao, Wenchong He +2
Deep learning for Earth imagery plays an increasingly important role in geoscience applications such as agriculture, ecology, and natural disaster management. Still, progress is of…
A Hierarchical Spatial Transformer for Massive Point Samples in Continuous Space
Wenchong He, Zhe Jiang, Tingsong Xiao +5
Transformers are widely used deep learning architectures. Existing transformers are mostly designed for sequences (texts or time series), images or videos, and graphs. This paper p…
Uncertainty Quantification of Deep Learning for Spatiotemporal Data: Challenges and Opportunities
Wenchong He, Zhe Jiang
With the advancement of GPS, remote sensing, and computational simulations, large amounts of geospatial and spatiotemporal data are being collected at an increasing speed. Such eme…
A Survey on Uncertainty Quantification Methods for Deep Learning
Wenchong He, Zhe Jiang, Tingsong Xiao +2
Deep neural networks (DNNs) have achieved tremendous success in computer vision, natural language processing, and scientific and engineering domains. However, DNNs can make unexpec…
An Explainer for Temporal Graph Neural Networks
Wenchong He, Minh N. Vu, Zhe Jiang +1
Temporal graph neural networks (TGNNs) have been widely used for modeling time-evolving graph-related tasks due to their ability to capture both graph topology dependency and non-l…
Deep Learning for Earth Image Segmentation based on Imperfect Polyline Labels with Annotation Errors
Zhe Jiang, Marcus Stephen Kirby, Wenchong He +1
In recent years, deep learning techniques (e.g., U-Net, DeepLab) have achieved tremendous success in image segmentation. The performance of these models heavily relies on high-qual…
Semi-supervised Learning with the EM Algorithm: A Comparative Study between Unstructured and Structured Prediction
Wenchong He, Zhe Jiang
Semi-supervised learning aims to learn prediction models from both labeled and unlabeled samples. There has been extensive research in this area. Among existing work, generative mi…
Spatial Classification With Limited Observations Based On Physics-Aware Structural Constraint
Arpan Man Sainju, Wenchong He, Zhe Jiang +2
Spatial classification with limited feature observations has been a challenging problem in machine learning. The problem exists in applications where only a subset of sensors are d…
Deep Neural Network for 3D Surface Segmentation based on Contour Tree Hierarchy
Wenchong He, Arpan Man Sainju, Zhe Jiang +1
Given a 3D surface defined by an elevation function on a 2D grid as well as non-spatial features observed at each pixel, the problem of surface segmentation aims to classify pixels…