Implicit Quantile Neural Networks for Jet Simulation and Correction
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Abstract (arXiv)
Reliable modeling of conditional densities is important for quantitative scientific fields such as particle physics. In domains outside physics, implicit quantile neural networks (IQN) have been shown to provide accurate models of conditional densities. We present a successful application of IQNs to jet simulation and correction using the tools and simulated data from the Compact Muon Solenoid (CMS) Open Data portal.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 26 references that are in the map. First posted on arXiv 22 Nov 2021.
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2 of 26 references.
References · 2
- 2019On the Convergence of Adam and Beyondcited by 841
- 2018Autoregressive Quantile Networks for Generative Modelingcited by 31
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