2 citations · 2 across the 1 of their papers we have counts for
4 papers
Diffusion Models in Simulation-Based Inference: A Tutorial Review
Jonas Arruda, Niels Bracher, Ullrich Köthe +2
Diffusion models have recently emerged as powerful learners for simulation-based inference (SBI), enabling fast and accurate estimation of latent parameters from simulated and real…
Stable Single-Pixel Contrastive Learning for Semantic and Geometric Tasks
Leonid Pogorelyuk, Niels Bracher, Aaron Verkleeren +2
We pilot a family of stable contrastive losses for learning pixel-level representations that jointly capture semantic and geometric information. Our approach maps each pixel of an…
Bridging Simulations and Observations: New Insights into Galaxy Formation Simulations via Out-of-Distribution Detection and Bayesian Model Comparison
Lingyi Zhou, Stefan T. Radev, William H. Oliver +3
Cosmological simulations are a powerful tool to advance our understanding of galaxy formation and many simulations model key properties of real galaxies. A question that naturally…
Aligning Motion-Blurred Images Using Contrastive Learning on Overcomplete Pixels
Leonid Pogorelyuk, Stefan T. Radev
We propose a new contrastive objective for learning overcomplete pixel-level features that are invariant to motion blur. Other invariances (e.g., pose, illumination, or weather) ca…