all AI news
Can denoising diffusion probabilistic models generate realistic astrophysical fields?. (arXiv:2211.12444v1 [astro-ph.CO])
Nov. 23, 2022, 2:12 a.m. | Nayantara Mudur, Douglas P. Finkbeiner
cs.LG updates on arXiv.org arxiv.org
Score-based generative models have emerged as alternatives to generative
adversarial networks (GANs) and normalizing flows for tasks involving learning
and sampling from complex image distributions. In this work we investigate the
ability of these models to generate fields in two astrophysical contexts: dark
matter mass density fields from cosmological simulations and images of
interstellar dust. We examine the fidelity of the sampled cosmological fields
relative to the true fields using three different metrics, and identify
potential issues to address. We …
More from arxiv.org / cs.LG updates on arXiv.org
The Perception-Robustness Tradeoff in Deterministic Image Restoration
2 days, 2 hours ago |
arxiv.org
Jobs in AI, ML, Big Data
Founding AI Engineer, Agents
@ Occam AI | New York
AI Engineer Intern, Agents
@ Occam AI | US
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne