5 papers
Pretrain Where? Investigating How Pretraining Data Diversity Impacts Geospatial Foundation Model Performance
Amandeep Kaur, Mirali Purohit, Gedeon Muhawenayo +2
New geospatial foundation models introduce a new model architecture and pretraining dataset, often sampled using different notions of data diversity. Performance differences are la…
MOMO: Mars Orbital Model Foundation Model for Mars Orbital Applications
Mirali Purohit, Bimal Gajera, Irish Mehta +8
We introduce MOMO, the first multi-sensor foundation model for Mars remote sensing. MOMO uses model merge to integrate representations learned independently from three key Martian…
Mars-Bench: A Benchmark for Evaluating Foundation Models for Mars Science Tasks
Mirali Purohit, Bimal Gajera, Vatsal Malaviya +6
Foundation models have enabled rapid progress across many specialized domains by leveraging large-scale pre-training on unlabeled data, demonstrating strong generalization to a var…
How Does the Spatial Distribution of Pre-training Data Affect Geospatial Foundation Models?
Mirali Purohit, Gedeon Muhawenayo, Esther Rolf +1
Foundation models have made rapid advances in many domains including Earth observation, where Geospatial Foundation Models (GFMs) can help address global challenges such as climate…
Beyond Performance: Quantifying and Mitigating Label Bias in LLMs
Yuval Reif, Roy Schwartz
Large language models (LLMs) have shown remarkable adaptability to diverse tasks, by leveraging context prompts containing instructions, or minimal input-output examples. However,…