6 citations · 15 across the 12 of their papers we have counts for
12 papers
Catching the Imposter: Self-Supervised Learning of Physical Coherence with Cross-Entity Feature Permutations
Aleksei Rozanov, Arvind Renganathan, Vipin Kumar
Scientific data often describe entities whose features are jointly governed by the laws of physics, yet existing self-supervised learning (SSL) objectives largely ignore this physi…
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…
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…
Uncertainty Quantification in Inverse Models in Hydrology
Somya Sharma Chatterjee, Rahul Ghosh, Arvind Renganathan +5
In hydrology, modeling streamflow remains a challenging task due to the limited availability of basin characteristics information such as soil geology and geomorphology. These char…
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…
Message Propagation Through Time: An Algorithm for Sequence Dependency Retention in Time Series Modeling
Shaoming Xu, Ankush Khandelwal, Arvind Renganathan +1
Time series modeling, a crucial area in science, often encounters challenges when training Machine Learning (ML) models like Recurrent Neural Networks (RNNs) using the conventional…
Koopman Invertible Autoencoder: Leveraging Forward and Backward Dynamics for Temporal Modeling
Kshitij Tayal, Arvind Renganathan, Rahul Ghosh +2
Accurate long-term predictions are the foundations for many machine learning applications and decision-making processes. However, building accurate long-term prediction models rema…
Entity Aware Modelling: A Survey
Rahul Ghosh, Haoyu Yang, Ankush Khandelwal +5
Personalized prediction of responses for individual entities caused by external drivers is vital across many disciplines. Recent machine learning (ML) advances have led to new stat…
Probabilistic Inverse Modeling: An Application in Hydrology
Somya Sharma, Rahul Ghosh, Arvind Renganathan +5
The astounding success of these methods has made it imperative to obtain more explainable and trustworthy estimates from these models. In hydrology, basin characteristics can be no…
Robust Inverse Framework using Knowledge-guided Self-Supervised Learning: An application to Hydrology
Rahul Ghosh, Arvind Renganathan, Kshitij Tayal +6
Machine Learning is beginning to provide state-of-the-art performance in a range of environmental applications such as streamflow prediction in a hydrologic basin. However, buildin…