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One flow to correct them all: improving simulations in high-energy
  physics with a single normalising flow and a switch

One flow to correct them all: improving simulations in high-energy physics with a single normalising flow and a switch

27 March 2024
Caio C. Daumann
M. Donega
Johannes Erdmann
M. Galli
Jan Lukas Späh
D. Valsecchi
    AI4CE
ArXiv (abs)PDFHTML

Papers citing "One flow to correct them all: improving simulations in high-energy physics with a single normalising flow and a switch"

15 / 15 papers shown
Title
Transport away your problems: Calibrating stochastic simulations with
  optimal transport
Transport away your problems: Calibrating stochastic simulations with optimal transport
Chris Pollard
Philipp Windischhofer
OT
32
8
0
19 Jul 2021
DCTRGAN: Improving the Precision of Generative Models with Reweighting
DCTRGAN: Improving the Precision of Generative Models with Reweighting
S. Diefenbacher
E. Eren
Gregor Kasieczka
A. Korol
Benjamin Nachman
David Shih
76
44
0
03 Sep 2020
Invertible Generative Modeling using Linear Rational Splines
Invertible Generative Modeling using Linear Rational Splines
H. M. Dolatabadi
S. Erfani
C. Leckie
89
65
0
15 Jan 2020
Normalizing Flows for Probabilistic Modeling and Inference
Normalizing Flows for Probabilistic Modeling and Inference
George Papamakarios
Eric T. Nalisnick
Danilo Jimenez Rezende
S. Mohamed
Balaji Lakshminarayanan
TPMAI4CE
213
1,718
0
05 Dec 2019
PyTorch: An Imperative Style, High-Performance Deep Learning Library
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke
Sam Gross
Francisco Massa
Adam Lerer
James Bradbury
...
Sasank Chilamkurthy
Benoit Steiner
Lu Fang
Junjie Bai
Soumith Chintala
ODL
568
42,677
0
03 Dec 2019
Neural Networks for Full Phase-space Reweighting and Parameter Tuning
Neural Networks for Full Phase-space Reweighting and Parameter Tuning
Anders Andreassen
Benjamin Nachman
76
88
0
18 Jul 2019
Cubic-Spline Flows
Cubic-Spline Flows
Conor Durkan
Artur Bekasov
Iain Murray
George Papamakarios
TPM
95
58
0
05 Jun 2019
Neural Importance Sampling
Neural Importance Sampling
Thomas Müller
Brian McWilliams
Fabrice Rousselle
Markus Gross
Jan Novák
73
364
0
11 Aug 2018
Glow: Generative Flow with Invertible 1x1 Convolutions
Glow: Generative Flow with Invertible 1x1 Convolutions
Diederik P. Kingma
Prafulla Dhariwal
BDLDRL
308
3,144
0
09 Jul 2018
Masked Autoregressive Flow for Density Estimation
Masked Autoregressive Flow for Density Estimation
George Papamakarios
Theo Pavlakou
Iain Murray
224
1,360
0
19 May 2017
Revisiting Classifier Two-Sample Tests
Revisiting Classifier Two-Sample Tests
David Lopez-Paz
Maxime Oquab
192
406
0
20 Oct 2016
Input Convex Neural Networks
Input Convex Neural Networks
Brandon Amos
Lei Xu
J. Zico Kolter
289
625
0
22 Sep 2016
Reweighting with Boosted Decision Trees
Reweighting with Boosted Decision Trees
A. Rogozhnikov
39
126
0
20 Aug 2016
SGDR: Stochastic Gradient Descent with Warm Restarts
SGDR: Stochastic Gradient Descent with Warm Restarts
I. Loshchilov
Frank Hutter
ODL
350
8,179
0
13 Aug 2016
MADE: Masked Autoencoder for Distribution Estimation
MADE: Masked Autoencoder for Distribution Estimation
M. Germain
Karol Gregor
Iain Murray
Hugo Larochelle
OODSyDaUQCV
189
874
0
12 Feb 2015
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