6 papers · 1 filter
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…
Hierarchical Conditional Multi-Task Learning for Streamflow Modeling
Shaoming Xu, Arvind Renganathan, Ankush Khandelwal +9
Streamflow, vital for water resource management, is governed by complex hydrological systems involving intermediate processes driven by meteorological forces. While deep learning m…
ExoTST: Exogenous-Aware Temporal Sequence Transformer for Time Series Prediction
Kshitij Tayal, Arvind Renganathan, Xiaowei Jia +2
Accurate long-term predictions are the foundations for many machine learning applications and decision-making processes. Traditional time series approaches for prediction often foc…
Task Aware Modulation using Representation Learning: An Approach for Few Shot Learning in Environmental Systems
Arvind Renganathan, Rahul Ghosh, Ankush Khandelwal +1
We introduce TAM-RL (Task Aware Modulation using Representation Learning), a novel multimodal meta-learning framework for few-shot learning in heterogeneous systems, designed for s…