1 citations · 1 across the 7 of their papers we have counts for
7 papers
M3R: Localized Rainfall Nowcasting with Meteorology-Informed MultiModal Attention
Sanjeev Panta, Rhett M Morvant, Xu Yuan +2
Accurate and timely rainfall nowcasting is crucial for disaster mitigation and water resource management. Despite recent advances in deep learning, precipitation prediction remains…
Resource Heterogeneity-Aware and Utilization-Enhanced Scheduling for Deep Learning Clusters
Abeda Sultana, Nabin Pakka, Fei Xu +3
Scheduling deep learning (DL) models to train on powerful clusters with accelerators like GPUs and TPUs, presently falls short, either lacking fine-grained heterogeneity awareness…
SEAFL: Enhancing Efficiency in Semi-Asynchronous Federated Learning through Adaptive Aggregation and Selective Training
Md Sirajul Islam, Sanjeev Panta, Fei Xu +3
Federated Learning (FL) is a promising distributed machine learning framework that allows collaborative learning of a global model across decentralized devices without uploading th…
Regional Weather Variable Predictions by Machine Learning with Near-Surface Observational and Atmospheric Numerical Data
Yihe Zhang, Bryce Turney, Purushottam Sigdel +11
Accurate and timely regional weather prediction is vital for sectors dependent on weather-related decisions. Traditional prediction methods, based on atmospheric equations, often s…
FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering
Md Sirajul Islam, Simin Javaherian, Fei Xu +3
Federated learning (FL) is an emerging distributed machine learning paradigm that enables collaborative training of machine learning models over decentralized devices without expos…
DreamScape: 3D Scene Creation via Gaussian Splatting joint Correlation Modeling
Yueming Zhao, Xuening Yuan, Hongyu Yang +1
Recent advances in text-to-3D creation integrate the potent prior of Diffusion Models from text-to-image generation into 3D domain. Nevertheless, generating 3D scenes with multiple…
FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering
Md Sirajul Islam, Simin Javaherian, Fei Xu +3
Federated learning (FL) is an emerging distributed machine learning paradigm enabling collaborative model training on decentralized devices without exposing their local data. A key…