Particle/Astro-Machine Learning Seminar

Probabilistic Neural Networks for Cosmology

While our understanding of the Universe's composition and formation has
advanced in the last two decades, fundamental questions persist. The
nature of the majority of the Universe, comprising 27% Dark Matter and
68% Dark Energy, remains unknown. To unveil these mysteries, astronomers
are constructing ambitious telescopes and sky surveys. LSST and Euclid
will soon come online, surveying vast portions of the sky with
unprecedented depth.
To deal with this volume of data, machine learning methods are planned

Neural Networks and Quantum Field Theory

We propose a theoretical understanding of neural networks in terms of Wilsonian effective field theory. The correspondence relies on the fact that many asymptotic neural networks are drawn from Gaussian processes, the analog of non-interacting field theories. Moving away from the asymptotic limit yields a non-Gaussian process and corresponds to turning on particle in- teractions, allowing for the computation of correlation functions of neural network outputs with Feynman diagrams.


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