1 citations · 2 across the 5 of their papers we have counts for
5 papers
A Markov Random Field model for Hypergraph-based Machine Learning
Bohan Tang, Keyue Jiang, Laura Toni +2
Understanding the data-generating process is essential for building machine learning models that generalise well while ensuring robustness and interpretability. This paper addresse…
Learning Hypergraphs From Signals With Dual Smoothness Prior
Bohan Tang, Siheng Chen, Xiaowen Dong
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…
Collaborative Uncertainty Benefits Multi-Agent Multi-Modal Trajectory Forecasting
Bohan Tang, Yiqi Zhong, Chenxin Xu +5
In multi-modal multi-agent trajectory forecasting, two major challenges have not been fully tackled: 1) how to measure the uncertainty brought by the interaction module that causes…
Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational Reasoning
Chenxin Xu, Yuxi Wei, Bohan Tang +3
Demystifying the interactions among multiple agents from their past trajectories is fundamental to precise and interpretable trajectory prediction. However, previous works mainly c…
Collaborative Uncertainty in Multi-Agent Trajectory Forecasting
Bohan Tang, Yiqi Zhong, Ulrich Neumann +3
Uncertainty modeling is critical in trajectory forecasting systems for both interpretation and safety reasons. To better predict the future trajectories of multiple agents, recent…