4 papers
Towards Knowledge Guided Pretraining Approaches for Multimodal Foundation Models: Applications in Remote Sensing
Praveen Ravirathinam, Ajitesh Parthasarathy, Ankush Khandelwal +2
Self-supervised learning has emerged as a powerful paradigm for pretraining foundation models using large-scale data. Existing pretraining approaches predominantly rely on masked r…
Task Aware Modulation Using Representation Learning for Upsaling of Terrestrial Carbon Fluxes
Aleksei Rozanov, Arvind Renganathan, Vipin Kumar
Accurately upscaling terrestrial carbon fluxes is central to estimating the global carbon budget, yet remains challenging due to the sparse and regionally biased distribution of gr…
CarbonBench: A Global Benchmark for Upscaling of Carbon Fluxes Using Zero-Shot Learning
Aleksei Rozanov, Arvind Renganathan, Yimeng Zhang +1
Accurately quantifying terrestrial carbon exchange is essential for climate policy and carbon accounting, yet models must generalize to ecosystems underrepresented in sparse eddy c…
Hierarchically Disentangled Recurrent Network for Factorizing System Dynamics of Multi-scale Systems: An application on Hydrological Systems
Rahul Ghosh, Arvind Renganathan, Zac McEachran +6
We present a framework for modeling multi-scale processes, and study its performance in the context of streamflow forecasting in hydrology. Specifically, we propose a novel hierarc…