6 citations · 12 across the 8 of their papers we have counts for
8 papers
ExEBench: Benchmarking Foundation Models on Extreme Earth Events
Shan Zhao, Zhitong Xiong, Jie Zhao +1
Our planet is facing increasingly frequent extreme events, which pose major risks to human lives and ecosystems. Recent advances in machine learning (ML), especially with foundatio…
Beyond Grid Data: Exploring Graph Neural Networks for Earth Observation
Shan Zhao, Zhaiyu Chen, Zhitong Xiong +3
Earth Observation (EO) data analysis has been significantly revolutionized by deep learning (DL), with applications typically limited to grid-like data structures. Graph Neural Net…
Causal Graph Neural Networks for Wildfire Danger Prediction
Shan Zhao, Ioannis Prapas, Ilektra Karasante +4
Wildfire forecasting is notoriously hard due to the complex interplay of different factors such as weather conditions, vegetation types and human activities. Deep learning models s…
Efficient Subseasonal Weather Forecast using Teleconnection-informed Transformers
Shan Zhao, Zhitong Xiong, Xiao Xiang Zhu
Subseasonal forecasting, which is pivotal for agriculture, water resource management, and early warning of disasters, faces challenges due to the chaotic nature of the atmosphere.…
Exploring Geometric Deep Learning For Precipitation Nowcasting
Shan Zhao, Sudipan Saha, Zhitong Xiong +2
Precipitation nowcasting (up to a few hours) remains a challenge due to the highly complex local interactions that need to be captured accurately. Convolutional Neural Networks rel…
Domain Re-Modulation for Few-Shot Generative Domain Adaptation
Yi Wu, Ziqiang Li, Chaoyue Wang +4
In this study, we delve into the task of few-shot Generative Domain Adaptation (GDA), which involves transferring a pre-trained generator from one domain to a new domain using only…
Reiterative Domain Aware Multi-Target Adaptation
Sudipan Saha, Shan Zhao, Nasrullah Sheikh +1
Most domain adaptation methods focus on single-source-single-target adaptation settings. Multi-target domain adaptation is a powerful extension in which a single classifier is lear…
The Utility of General Domain Transfer Learning for Medical Language Tasks
Daniel Ranti, Katie Hanss, Shan Zhao +4
The purpose of this study is to analyze the efficacy of transfer learning techniques and transformer-based models as applied to medical natural language processing (NLP) tasks, spe…