Generative transformers and how to evaluate them
Abstract:
Particle/Astro-Machine Learning Seminar
Probabilistic Neural Networks for Cosmology
Abstract:
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
Applications of Machine Learning to Predicting Core-collapse Supernova Explosion Outcomes
ABSTRACT:
Application of Inferno to a Top Pair Cross Section Measurement with CMS Open Data
Abstract:
Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation
Abstract:
Robust Machine Learning for Science and Society
Abstract:
KamNet: An Integrated Spatiotemporal Deep Neural Network for Rare Event Search in KamLAND-Zen
Abstract:
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.
Virtual Neural Embedding: Learning the Embedding of the Manifold of Physics Data
Abstract:
Conditional Invertible Neural Networks for HEP
Abstract:
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