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Found 15 papers, 6 papers with code
Hyper-YOLO: When Visual Object Detection Meets Hypergraph Computation
We introduce Hyper-YOLO, a new object detection method that integrates hypergraph computations to capture the complex high-order correlations among visual features.
LightHGNN: Distilling Hypergraph Neural Networks into MLPs for $100\times$ Faster Inference
Experiments on eight hypergraph datasets demonstrate that even without hypergraph dependency, the proposed LightHGNNs can still achieve competitive or even better performance than HGNNs and outperform vanilla MLPs by $16. 3$ on average.
Hypergraph Isomorphism Computation
The isomorphism problem is a fundamental problem in network analysis, which involves capturing both low-order and high-order structural information.
Nested Elimination: A Simple Algorithm for Best-Item Identification from Choice-Based Feedback
NE is simple in structure, easy to implement, and has a strong theoretical guarantee for sample complexity.
Intelligent wayfinding vehicle design based on visual recognition
In this project, an intelligent drug delivery car is designed and manufactured, which can recognize the road route and the room number of the target ward through visual recognition technology.
Deep Hypergraph Structure Learning
To address this issue, we design a general paradigm of deep hypergraph structure learning, namely DeepHGSL, to optimize the hypergraph structure for hypergraph-based representation learning.
On A Mallows-type Model For (Ranked) Choices
We consider a preference learning setting where every participant chooses an ordered list of $k$ most preferred items among a displayed set of candidates.
Optimal Distribution Design for Irregular Repetition Slotted ALOHA with Multi-Packet Reception
Associated with multi-packet reception at the access point, irregular repetition slotted ALOHA (IRSA) holds a great potential in improving the access capacity of massive machine type communication systems.
Dual Channel Hypergraph Collaborative Filtering
Second, the hypergraph structure is employed for modeling users and items with explicit hybrid high-order correlations.
DHGNN: Dynamic Hypergraph Neural Networks
Then hypergraph convolution is introduced to encode high-order data relations in a hypergraph structure.
PVRNet: Point-View Relation Neural Network for 3D Shape Recognition
More specifically, based on the relation score module, the point-single-view fusion feature is first extracted by fusing the point cloud feature and each single view feature with point-singe-view relation, then the point-multi-view fusion feature is extracted by fusing the point cloud feature and the features of different number of views with point-multi-view relation.
MeshNet: Mesh Neural Network for 3D Shape Representation
However, there is little effort on using mesh data in recent years, due to the complexity and irregularity of mesh data.
Hypergraph Neural Networks
In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure.
PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition
With the recent proliferation of deep learning, various deep models with different representations have achieved the state-of-the-art performance.
GVCNN: Group-View Convolutional Neural Networks for 3D Shape Recognition
The proposed GVCNN framework is composed of a hierarchical view-group-shape architecture, i. e., from the view level, the group level and the shape level, which are organized using a grouping strategy.
