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The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning

Jun 30, 2021
Anders Andreassen, Yasaman Bahri, Behnam Neyshabur, Rebecca Roelofs

* 27 pages, 25 figures 

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Scaffolding Simulations with Deep Learning for High-dimensional Deconvolution

May 10, 2021
Anders Andreassen, Patrick T. Komiske, Eric M. Metodiev, Benjamin Nachman, Adi Suresh, Jesse Thaler

* ICLR simDL workshop 2021 (
* 6 pages, 1 figure, 1 table 

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Understanding the Failure Modes of Out-of-Distribution Generalization

Oct 29, 2020
Vaishnavh Nagarajan, Anders Andreassen, Behnam Neyshabur

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Parameter Estimation using Neural Networks in the Presence of Detector Effects

Oct 22, 2020
Anders Andreassen, Shih-Chieh Hsu, Benjamin Nachman, Natchanon Suaysom, Adi Suresh

* 15 pages, 13 figures, 4 tables; v2: has small modifications from additional feedback 

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Asymptotics of Wide Convolutional Neural Networks

Aug 19, 2020
Anders Andreassen, Ethan Dyer

* 23 pages, 12 figures 

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Simulation Assisted Likelihood-free Anomaly Detection

Jan 14, 2020
Anders Andreassen, Benjamin Nachman, David Shih

* 19 pages, 9 figures 

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OmniFold: A Method to Simultaneously Unfold All Observables

Nov 20, 2019
Anders Andreassen, Patrick T. Komiske, Eric M. Metodiev, Benjamin Nachman, Jesse Thaler

* 7 pages, 3 figures, 1 table, 1 poem 

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Neural Networks for Full Phase-space Reweighting and Parameter Tuning

Aug 26, 2019
Anders Andreassen, Benjamin Nachman

* 7 pages, 3 figures; v2 has updated citations and clarifications; v3 has a new appendix with an alternative fitting method 

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JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics

Apr 25, 2018
Anders Andreassen, Ilya Feige, Christopher Frye, Matthew D. Schwartz

* 37 pages, 24 figures 

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