4 citations · 7 across the 13 of their papers we have counts for
5 papers · 1 filter
Retrieval-Augmented Foundation Models for Water Level Prediction in the Everglades
Rahuul Rangaraj, Jimeng Shi, Rajendra Paudel +2
Accurate water level forecasting in the Everglades is essential for flood mitigation, drought management, water resource planning, and biodiversity conservation. While recent time-…
How Effective are Large Time Series Models in Hydrology? A Study on Water Level Forecasting in Everglades
Rahuul Rangaraj, Jimeng Shi, Azam Shirali +3
The Everglades play a crucial role in flood and drought regulation, water resource planning, and ecosystem management in the surrounding regions. However, traditional physics-based…
Deep Learning and Foundation Models for Weather Prediction: A Survey
Jimeng Shi, Azam Shirali, Bowen Jin +10
Physics-based numerical models have been the bedrock of atmospheric sciences for decades, offering robust solutions but often at the cost of significant computational resources. De…
Boosting Deep Ensembles with Learning Rate Tuning
Hongpeng Jin, Yanzhao Wu
The Learning Rate (LR) has a high impact on deep learning training performance. A common practice is to train a Deep Neural Network (DNN) multiple times with different LR policies…
DA-MoE: Towards Dynamic Expert Allocation for Mixture-of-Experts Models
Maryam Akhavan Aghdam, Hongpeng Jin, Yanzhao Wu
Transformer-based Mixture-of-Experts (MoE) models have been driving several recent technological advancements in Natural Language Processing (NLP). These MoE models adopt a router…