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 …
A survey on graph neural networks and graph transformers in computer vision: A task-oriented perspective
Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …
Particle transformer for jet tagging
Jet tagging is a critical yet challenging classification task in particle physics. While deep
learning has transformed jet tagging and significantly improved performance, the lack of a …
learning has transformed jet tagging and significantly improved performance, the lack of a …
A review of deep learning-based semantic segmentation for point cloud
J Zhang, X Zhao, Z Chen, Z Lu - IEEE access, 2019 - ieeexplore.ieee.org
In recent years, the popularity of depth sensors and 3D scanners has led to a rapid
development of 3D point clouds. Semantic segmentation of point cloud, as a key step in …
development of 3D point clouds. Semantic segmentation of point cloud, as a key step in …
Geometric back-projection network for point cloud classification
As the basic task of point cloud analysis, classification is fundamental but always
challenging. To address some unsolved problems of existing methods, we propose a …
challenging. To address some unsolved problems of existing methods, we propose a …
Deep learning-based 3D point cloud classification: A systematic survey and outlook
In recent years, point cloud representation has become one of the research hotspots in the
field of computer vision, and has been widely used in many fields, such as autonomous …
field of computer vision, and has been widely used in many fields, such as autonomous …
Attention-based point cloud edge sampling
Point cloud sampling is a less explored research topic for this data representation. The most
commonly used sampling methods are still classical random sampling and farthest point …
commonly used sampling methods are still classical random sampling and farthest point …
3DCTN: 3D convolution-transformer network for point cloud classification
Point cloud classification is a fundamental task in 3D applications. However, it is challenging
to achieve effective feature learning due to the irregularity and unordered nature of point …
to achieve effective feature learning due to the irregularity and unordered nature of point …
Graph neural networks at the Large Hadron Collider
From raw detector activations to reconstructed particles, data at the Large Hadron Collider
(LHC) are sparse, irregular, heterogeneous and highly relational in nature. Graph neural …
(LHC) are sparse, irregular, heterogeneous and highly relational in nature. Graph neural …
A kernel correlation-based approach to adaptively acquire local features for learning 3D point clouds
Abstract 3D models are used in a variety of CAX fields, and their key is 3D data geometry
and semantic perception. However, semantic learning of 3D point clouds is a challenge due …
and semantic perception. However, semantic learning of 3D point clouds is a challenge due …