3 papers
cs.CV2026
Improving Lunar Topography with Deep Learning Schrödinger Bridges
Matthew Repasky, Erwan Mazarico, Michael K. Barker +3
Increasing the resolution of planetary topography models can enable a better understanding of surface processes and geomorphology; however, existing analytical super-resolution met…
stat.AP2025
Modeling Discrete Coating Degradation Events via Hawkes Processes
Matthew Repasky, Henry Yuchi, Fritz Friedersdorf +1
Forecasting the degradation of coated materials has long been a topic of critical interest in engineering, as it has enormous implications for both system maintenance and sustainab…
stat.ML2024
Posterior sampling via Langevin dynamics based on generative priors
Vishal Purohit, Matthew Repasky, Jianfeng Lu +3
Posterior sampling in high-dimensional spaces using generative models holds significant promise for various applications, including but not limited to inverse problems and guided g…