Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox

Picture for Anders Andreassen

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 

  Access Paper or Ask Questions

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 (https://simdl.github.io/files/12.pdf
* 6 pages, 1 figure, 1 table 

  Access Paper or Ask Questions

Understanding the Failure Modes of Out-of-Distribution Generalization


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


  Access Paper or Ask Questions

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 

  Access Paper or Ask Questions

Asymptotics of Wide Convolutional Neural Networks


Aug 19, 2020
Anders Andreassen, Ethan Dyer

* 23 pages, 12 figures 

  Access Paper or Ask Questions

Simulation Assisted Likelihood-free Anomaly Detection


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

* 19 pages, 9 figures 

  Access Paper or Ask Questions

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 

  Access Paper or Ask Questions

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 

  Access Paper or Ask Questions

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 

  Access Paper or Ask Questions