47 citations · 79 across the 10 of their papers we have counts for
10 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,…
ConeQuest: A Benchmark for Cone Segmentation on Mars
Mirali Purohit, Jacob Adler, Hannah Kerner
Over the years, space scientists have collected terabytes of Mars data from satellites and rovers. One important set of features identified in Mars orbital images is pitted cones,…
Lightweight, Pre-trained Transformers for Remote Sensing Timeseries
Gabriel Tseng, Ruben Cartuyvels, Ivan Zvonkov +3
Machine learning methods for satellite data have a range of societally relevant applications, but labels used to train models can be difficult or impossible to acquire. Self-superv…
In-BoXBART: Get Instructions into Biomedical Multi-Task Learning
Mihir Parmar, Swaroop Mishra, Mirali Purohit +3
Single-task models have proven pivotal in solving specific tasks; however, they have limitations in real-world applications where multi-tasking is necessary and domain shifts are e…
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi +37
How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benc…
CinC-GAN for Effective F0 prediction for Whisper-to-Normal Speech Conversion
Maitreya Patel, Mirali Purohit, Jui Shah +1
Recently, Generative Adversarial Networks (GAN)-based methods have shown remarkable performance for the Voice Conversion and WHiSPer-to-normal SPeeCH (WHSP2SPCH) conversion. One of…