Picture for Tilman Plehn

Tilman Plehn

Constraining the Higgs Potential with Neural Simulation-based Inference for Di-Higgs Production

May 24, 2024
Viaarxiv icon

Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics

Add code
May 23, 2024
Viaarxiv icon

The Landscape of Unfolding with Machine Learning

Apr 29, 2024
Viaarxiv icon

Anomalies, Representations, and Self-Supervision

Add code
Jan 11, 2023
Figure 1 for Anomalies, Representations, and Self-Supervision
Figure 2 for Anomalies, Representations, and Self-Supervision
Figure 3 for Anomalies, Representations, and Self-Supervision
Figure 4 for Anomalies, Representations, and Self-Supervision
Viaarxiv icon

Generative Networks for Precision Enthusiasts

Oct 22, 2021
Figure 1 for Generative Networks for Precision Enthusiasts
Figure 2 for Generative Networks for Precision Enthusiasts
Figure 3 for Generative Networks for Precision Enthusiasts
Figure 4 for Generative Networks for Precision Enthusiasts
Viaarxiv icon

Shared Data and Algorithms for Deep Learning in Fundamental Physics

Add code
Jul 01, 2021
Figure 1 for Shared Data and Algorithms for Deep Learning in Fundamental Physics
Figure 2 for Shared Data and Algorithms for Deep Learning in Fundamental Physics
Figure 3 for Shared Data and Algorithms for Deep Learning in Fundamental Physics
Figure 4 for Shared Data and Algorithms for Deep Learning in Fundamental Physics
Viaarxiv icon

Better Latent Spaces for Better Autoencoders

Add code
Apr 16, 2021
Figure 1 for Better Latent Spaces for Better Autoencoders
Figure 2 for Better Latent Spaces for Better Autoencoders
Figure 3 for Better Latent Spaces for Better Autoencoders
Figure 4 for Better Latent Spaces for Better Autoencoders
Viaarxiv icon

GANplifying Event Samples

Sep 16, 2020
Figure 1 for GANplifying Event Samples
Figure 2 for GANplifying Event Samples
Figure 3 for GANplifying Event Samples
Figure 4 for GANplifying Event Samples
Viaarxiv icon