Variational Autoencoders for New Physics Mining at the Large Hadron
Collider

Variational Autoencoders for New Physics Mining at the Large Hadron Collider

26 November 2018

Thong Q. Nguyen

Papers citing "Variational Autoencoders for New Physics Mining at the Large Hadron Collider"

10 / 10 papers shown

Title
TRANSIT your events into a new mass: Fast background interpolation for weakly-supervised anomaly searches Ivan Oleksiyuk Svyatoslav Voloshynovskiy Tobias Golling DiffM 63 0 0 06 Mar 2025
Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data Hiroshi Takahashi Tomoharu Iwata Atsutoshi Kumagai Yuuki Yamanaka 62 1 0 29 May 2024
Detector monitoring with artificial neural networks at the CMS experiment at the CERN Large Hadron Collider Adrian Alan Pol G. Cerminara Cécile Germain M. Pierini Agrima Seth 20 21 0 27 Jul 2018
Novelty Detection Meets Collider Physics Jan Hajer Ying-Ying Li Tao Liu He Wang 73 103 0 26 Jul 2018
Topology classification with deep learning to improve real-time event selection at the LHC Thong Q. Nguyen Daniel Weitekamp D. Anderson R. Castello O. Cerri M. Pierini M. Spiropulu J. Vlimant 41 34 0 29 Jun 2018
Fast inference of deep neural networks in FPGAs for particle physics Javier Mauricio Duarte Song Han Philip C. Harris S. Jindariani E. Kreinar ... J. Ngadiuba M. Pierini R. Rivera N. Tran Zhenbin Wu AI4CE 121 390 0 16 Apr 2018
VAE with a VampPrior Jakub M. Tomczak Max Welling GAN BDL 55 628 0 19 May 2017
Generative Temporal Models with Memory Mevlana Gemici Chia-Chun Hung Adam Santoro Greg Wayne S. Mohamed Danilo Jimenez Rezende David Amos Timothy Lillicrap 33 57 0 15 Feb 2017
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 850 149,474 0 22 Dec 2014
Auto-Encoding Variational Bayes Diederik P. Kingma Max Welling BDL 367 16,962 0 20 Dec 2013