#iaifi — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #iaifi, aggregated by home.social.
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New #openaccess publication #SciPost #Physics
A Lorentz-equivariant transformer for all of the LHC
Johann Brehmer, Víctor Bresó, Pim de Haan, Tilman Plehn, Huilin Qu, Jonas Spinner, Jesse Thaler
SciPost Phys. 19, 108 (2025)
https://scipost.org/SciPostPhys.19.4.108 -
New #openaccess publication #SciPost #Physics Core
Towards universal unfolding of detector effects in high-energy physics using denoising diffusion probabilistic models
Camila Pazos, Shuchin Aeron, Pierre-Hugues Beauchemin, Vincent Croft, Zhengyan Huan, Martin Klassen, Taritree Wongjirad
SciPost Phys. Core 8, 064 (2025)
https://scipost.org/SciPostPhysCore.8.4.064 -
New #openaccess publication #SciPost #Physics
Learning the simplicity of scattering amplitudes
Clifford Cheung, Aurélien Dersy, Matthew D. Schwartz
SciPost Phys. 18, 040 (2025)
https://scipost.org/SciPostPhys.18.2.040 -
New #openaccess publication #SciPost #Physics
Open string stub as an auxiliary string field
Harold Erbin, Atakan Hilmi Firat
SciPost Phys. 17, 044 (2024)
https://scipost.org/SciPostPhys.17.2.044 -
New #openaccess publication #SciPost #Physics
Goodness of fit by Neyman-Pearson testing
Gaia Grosso, Marco Letizia, Maurizio Pierini, Andrea Wulzer
SciPost Phys. 16, 123 (2024)
https://scipost.org/SciPostPhys.16.5.123 -
New #openaccess publication #SciPost #Physics
EPiC-GAN: Equivariant point cloud generation for particle jets
Erik Buhmann, Gregor Kasieczka, Jesse Thaler
SciPost Phys. 15, 130 (2023)
https://scipost.org/SciPostPhys.15.4.130#UH
#CDCS
#MIT
#IAIFI
#BMBF
#DFG
#FriedrichNaumannStiftung
#NSF
#DOE -
Next was a great talk by @jascha on learned optimizers at #IAIFI. This work is going after the important problem of moving away from hand-designed optimizers in deep learning, and Sohl-Dickstein shows some promising results here https://www.youtube.com/watch?v=FrqLLRpAdL0 (4/11)
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📢New paper (finally...) out today📢 'EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets' with Erik (#UniHamburg too) and Jesse (#IAIFI)
A short summary below, full paper at https://arxiv.org/abs/2301.08128
One useful way to represent data from particle physics collisions is a point cloud: each collision event is a cloud of points & each point has a position in space (the position of the specific sensor or particle) and some additional features attached (for example the energy)