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The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics
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 …
emerging, and aims to leverage recent breakthroughs in anomaly detection and machine …
Deep anomaly detection on set data: Survey and comparison
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 …
novel modalities, such as point-cloud data produced by lidars. Novel methods including …
Object-centric learning with slot attention
Learning object-centric representations of complex scenes is a promising step towards
enabling efficient abstract reasoning from low-level perceptual features. Yet, most deep …
enabling efficient abstract reasoning from low-level perceptual features. Yet, most deep …
Learning the best pooling strategy for visual semantic embedding
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 …
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
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 …
Initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged …
Decomposing 3d scenes into objects via unsupervised volume segmentation
K Stelzner, K Kersting, AR Kosiorek - ar**s on D-dimensional domains which are
invariant to an appropriate group action and which separate orbits. The motivation for this …
invariant to an appropriate group action and which separate orbits. The motivation for this …