Web: http://arxiv.org/abs/2112.09822

June 17, 2022, 1:11 a.m. | Saeed Saremi, Rupesh Kumar Srivastava

cs.LG updates on arXiv.org arxiv.org

We formally map the problem of sampling from an unknown distribution with a
density in $\mathbb{R}^d$ to the problem of learning and sampling a smoother
density in $\mathbb{R}^{Md}$ obtained by convolution with a fixed factorial
kernel: the new density is referred to as M-density and the kernel as
multimeasurement noise model (MNM). The M-density in $\mathbb{R}^{Md}$ is
smoother than the original density in $\mathbb{R}^d$, easier to learn and
sample from, yet for large $M$ the two problems are mathematically equivalent …

arxiv ml models

More from arxiv.org / cs.LG updates on arXiv.org

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY