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Found 8 papers, 3 papers with code
Hypergraph Node Classification With Graph Neural Networks
However, in this paper, we theoretically demonstrate that, in the context of node classification, most HyperGNNs can be approximated using a GNN with a weighted clique expansion of the hypergraph.
Hypergraph Transformer for Semi-Supervised Classification
Hypergraphs play a pivotal role in the modelling of data featuring higher-order relations involving more than two entities.
Hypergraph-MLP: Learning on Hypergraphs without Message Passing
Hypergraphs are vital in modelling data with higher-order relations containing more than two entities, gaining prominence in machine learning and signal processing.
Hypergraph Structure Inference From Data Under Smoothness Prior
However, existing methods either adopt simple pre-defined rules that fail to precisely capture the distribution of the potential hypergraph structure, or learn a mapping between hypergraph structures and node features but require a large amount of labelled data, i. e., pre-existing hypergraph structures, for training.
Learning Hypergraphs From Signals With Dual Smoothness Prior
Hypergraph structure learning, which aims to learn the hypergraph structures from the observed signals to capture the intrinsic high-order relationships among the entities, becomes crucial when a hypergraph topology is not readily available in the datasets.
Collaborative Uncertainty Benefits Multi-Agent Multi-Modal Trajectory Forecasting
Further, we apply the proposed framework to current SOTA multi-agent multi-modal forecasting systems as a plugin module, which enables the SOTA systems to 1) estimate the uncertainty in the multi-agent multi-modal trajectory forecasting task; 2) rank the multiple predictions and select the optimal one based on the estimated uncertainty.
Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational Reasoning
Demystifying the interactions among multiple agents from their past trajectories is fundamental to precise and interpretable trajectory prediction.
Collaborative Uncertainty in Multi-Agent Trajectory Forecasting
2) The results of trajectory forecasting benchmarks demonstrate that the CU-based framework steadily helps SOTA systems improve their performances.
