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Machine Learning Solutions for High Energy Physics: Applications to
  Electromagnetic Shower Generation, Flavor Tagging, and the Search for
  di-Higgs Production

Machine Learning Solutions for High Energy Physics: Applications to Electromagnetic Shower Generation, Flavor Tagging, and the Search for di-Higgs Production

12 March 2019
Michela Paganini
ArXivPDFHTML

Papers citing "Machine Learning Solutions for High Energy Physics: Applications to Electromagnetic Shower Generation, Flavor Tagging, and the Search for di-Higgs Production"

3 / 3 papers shown
Title
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn
Pieter Abbeel
Sergey Levine
OOD
323
11,681
0
09 Mar 2017
Geometric deep learning: going beyond Euclidean data
Geometric deep learning: going beyond Euclidean data
M. Bronstein
Joan Bruna
Yann LeCun
Arthur Szlam
P. Vandergheynst
GNN
250
3,236
0
24 Nov 2016
Fuzzy Jets
Fuzzy Jets
Lester W. Mackey
Benjamin Nachman
A. Schwartzman
Conrad Stansbury
14
12
0
07 Sep 2015
1