Unsupervised in-distribution anomaly detection of new physics through conditional density estimation

21 December 2020

Papers citing "Unsupervised in-distribution anomaly detection of new physics through conditional density estimation"

7 / 7 papers shown

Title
Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows Thorsten Buss F. Gaede Gregor Kasieczka Claudius Krause David Shih AI4CE 36 6 0 30 May 2024
Machine Learning in the Search for New Fundamental Physics G. Karagiorgi Gregor Kasieczka S. Kravitz Benjamin Nachman David Shih AI4CE 42 113 0 07 Dec 2021
Online-compatible Unsupervised Non-resonant Anomaly Detection Vinicius Mikuni Benjamin Nachman David Shih 27 35 0 11 Nov 2021
Challenges for Unsupervised Anomaly Detection in Particle Physics Katherine Fraser S. Homiller Rashmish K. Mishra B. Ostdiek M. Schwartz DRL 27 43 0 13 Oct 2021
Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection J. Collins P. Martín-Ramiro Benjamin Nachman David Shih 29 44 0 05 Apr 2021
A Living Review of Machine Learning for Particle Physics Matthew Feickert Benjamin Nachman KELM AI4CE 27 178 0 02 Feb 2021
Probabilistic Autoencoder Vanessa Böhm U. Seljak UQCV BDL DRL 21 32 0 09 Jun 2020