5 citations · 11 across the 7 of their papers we have counts for
8 papers
Spatiotemporal Pyramid Flow Matching for Climate Emulation
Jeremy Andrew Irvin, Jiaqi Han, Zikui Wang +6
Generative models have the potential to transform the way we emulate Earth's changing climate. Previous generative approaches rely on weather-scale autoregression for climate emula…
STARC-9: A Large-scale Dataset for Multi-Class Tissue Classification for CRC Histopathology
Barathi Subramanian, Rathinaraja Jeyaraj, Mitchell Nevin Peterson +5
Multi-class tissue-type classification of colorectal cancer (CRC) histopathologic images is a significant step in the development of downstream machine learning models for diagnosi…
UQ: Assessing Language Models on Unsolved Questions
Fan Nie, Ken Ziyu Liu, Zihao Wang +11
Benchmarks shape progress in AI research. A useful benchmark should be both difficult and realistic: questions should challenge frontier models while also reflecting real-world usa…
Many-Shot In-Context Learning in Multimodal Foundation Models
Yixing Jiang, Jeremy Irvin, Ji Hun Wang +3
Large language models are effective at few-shot in-context learning (ICL). Recent advancements in multimodal foundation models have enabled unprecedentedly long context windows, pr…
CloudTracks: A Dataset for Localizing Ship Tracks in Satellite Images of Clouds
Muhammad Ahmed Chaudhry, Lyna Kim, Jeremy Irvin +5
Clouds play a significant role in global temperature regulation through their effect on planetary albedo. Anthropogenic emissions of aerosols can alter the albedo of clouds, but th…
An Empirical Study of Automated Mislabel Detection in Real World Vision Datasets
Maya Srikanth, Jeremy Irvin, Brian Wesley Hill +3
Major advancements in computer vision can primarily be attributed to the use of labeled datasets. However, acquiring labels for datasets often results in errors which can harm mode…
USat: A Unified Self-Supervised Encoder for Multi-Sensor Satellite Imagery
Jeremy Irvin, Lucas Tao, Joanne Zhou +4
Large, self-supervised vision models have led to substantial advancements for automatically interpreting natural images. Recent works have begun tailoring these methods to remote s…
Weakly-semi-supervised object detection in remotely sensed imagery
Ji Hun Wang, Jeremy Irvin, Beri Kohen Behar +4
Deep learning for detecting objects in remotely sensed imagery can enable new technologies for important applications including mitigating climate change. However, these models oft…