22 citations · 55 across the 16 of their papers we have counts for
6 papers · 1 filter
WxC-Bench: A Novel Dataset for Weather and Climate Downstream Tasks
Rajat Shinde, Christopher E. Phillips, Kumar Ankur +10
High-quality machine learning (ML)-ready datasets play a foundational role in developing new artificial intelligence (AI) models or fine-tuning existing models for scientific appli…
Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications
Daniela Szwarcman, Sujit Roy, Paolo Fraccaro +33
This paper presents Prithvi-EO-2.0, a new geospatial foundation model that offers significant improvements over its predecessor, Prithvi-EO-1.0. Trained on 4.2 million global time…
AI Foundation Model for Heliophysics: Applications, Design, and Implementation
Sujit Roy, Talwinder Singh, Marcus Freitag +14
Deep learning-based methods have been widely researched in the areas of language and vision, demonstrating their capacity to understand long sequences of data and their usefulness…
On Evaluation of Vision Datasets and Models using Human Competency Frameworks
Rahul Ramachandran, Tejal Kulkarni, Charchit Sharma +2
Evaluating models and datasets in computer vision remains a challenging task, with most leaderboards relying solely on accuracy. While accuracy is a popular metric for model evalua…
Machine Learning Global Simulation of Nonlocal Gravity Wave Propagation
Aman Gupta, Aditi Sheshadri, Sujit Roy +3
Global climate models typically operate at a grid resolution of hundreds of kilometers and fail to resolve atmospheric mesoscale processes, e.g., clouds, precipitation, and gravity…
INDUS: Effective and Efficient Language Models for Scientific Applications
Bishwaranjan Bhattacharjee, Aashka Trivedi, Masayasu Muraoka +33
Large language models (LLMs) trained on general domain corpora showed remarkable results on natural language processing (NLP) tasks. However, previous research demonstrated LLMs tr…