Search Results for author:
Found 7 papers, 2 papers with code
Neural Active Learning Beyond Bandits
We study both stream-based and pool-based active learning with neural network approximations.
Foundation Models for Generalist Geospatial Artificial Intelligence
This paper introduces a first-of-a-kind framework for the efficient pre-training and fine-tuning of foundational models on extensive geospatial data.
AI Foundation Models for Weather and Climate: Applications, Design, and Implementation
In particular, we are witnessing the rise of AI foundation models that can perform competitively on multiple domain-specific downstream tasks.
Encoding Seasonal Climate Predictions for Demand Forecasting with Modular Neural Network
Current time-series forecasting problems use short-term weather attributes as exogenous inputs.
Class-Imbalanced Graph Learning without Class Rebalancing
In this work, we approach the root cause of class-imbalance bias from an topological paradigm.
Supply chain emission estimation using large language models
Large enterprises face a crucial imperative to achieve the Sustainable Development Goals (SDGs), especially goal 13, which focuses on combating climate change and its impacts.
Climate Impact Modelling Framework
Here we present a cloud-based modular framework for the deployment and operation of geospatial models, initially applied to climate impacts.
