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Found 5 papers, 0 papers with code
The Landscape and Challenges of HPC Research and LLMs
Recently, language models (LMs), especially large language models (LLMs), have revolutionized the field of deep learning.
Accelerating Domain-aware Deep Learning Models with Distributed Training
We perform extensive experiments on a dataset of 23 watersheds in a northern state of the U. S. and present our findings.
Transfer Learning Approaches for Knowledge Discovery in Grid-based Geo-Spatiotemporal Data
Extracting and meticulously analyzing geo-spatiotemporal features is crucial to recognize intricate underlying causes of natural events, such as floods.
HydroDeep -- A Knowledge Guided Deep Neural Network for Geo-Spatiotemporal Data Analysis
Due to limited evidence and complex causes of regional climate change, the confidence in predicting fluvial floods remains low.
A CASE STUDY ON OPTIMAL DEEP LEARNING MODEL FOR UAVS
Our work is mainly focused on autonomous flying drones where we establish a case study towards efficiency, robustness and accuracy of UAVs where we showed our results well supported through experiments.
