The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics

G Kasieczka, B Nachman, D Shih… - Reports on progress …, 2021‏ - iopscience.iop.org
A new paradigm for data-driven, model-agnostic new physics searches at colliders is
emerging, and aims to leverage recent breakthroughs in anomaly detection and machine …

Deep anomaly detection on set data: Survey and comparison

M Mašková, M Zorek, T Pevný, V Šmídl - Pattern Recognition, 2024‏ - Elsevier
Detecting anomalous samples in set data is a problem attracting increased interest due to
novel modalities, such as point-cloud data produced by lidars. Novel methods including …

Object-centric learning with slot attention

F Locatello, D Weissenborn… - Advances in neural …, 2020‏ - proceedings.neurips.cc
Learning object-centric representations of complex scenes is a promising step towards
enabling efficient abstract reasoning from low-level perceptual features. Yet, most deep …

Learning the best pooling strategy for visual semantic embedding

J Chen, H Hu, H Wu, Y Jiang… - Proceedings of the IEEE …, 2021‏ - openaccess.thecvf.com
Abstract Visual Semantic Embedding (VSE) is a dominant approach for vision-language
retrieval, which aims at learning a deep embedding space such that visual data are …

The dark machines anomaly score challenge: benchmark data and model independent event classification for the large hadron collider

T Aarrestad, M van Beekveld, M Bona, A Boveia… - SciPost Physics, 2022‏ - scipost.org
We describe the outcome of a data challenge conducted as part of the Dark Machines
Initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged …