297 citations · 880 across the 30 of their papers we have counts for
3 papers · 1 filter
New Methods and Datasets for Group Anomaly Detection From Fundamental Physics
Gregor Kasieczka, Benjamin Nachman, David Shih
The identification of anomalous overdensities in data - group or collective anomaly detection - is a rich problem with a large number of real world applications. However, it has re…
Latent Space Refinement for Deep Generative Models
Ramon Winterhalder, Marco Bellagente, Benjamin Nachman
Deep generative models are becoming widely used across science and industry for a variety of purposes. A common challenge is achieving a precise implicit or explicit representation…
Scaffolding Simulations with Deep Learning for High-dimensional Deconvolution
Anders Andreassen, Patrick T. Komiske, Eric M. Metodiev +3
A common setting for scientific inference is the ability to sample from a high-fidelity forward model (simulation) without having an explicit probability density of the data. We pr…