Search Results for author: Bohan Tang

Found 8 papers, 3 papers with code

Hypergraph Transformer for Semi-Supervised Classification

1 code implementation18 Dec 2023 Zexi Liu, Bohan Tang, Ziyuan Ye, Xiaowen Dong, Siheng Chen, Yanfeng Wang

Hypergraphs play a pivotal role in the modelling of data featuring higher-order relations involving more than two entities.

Classification Node Classification +1

Hypergraph-MLP: Learning on Hypergraphs without Message Passing

1 code implementation15 Dec 2023 Bohan Tang, Siheng Chen, Xiaowen Dong

Hypergraphs are vital in modelling data with higher-order relations containing more than two entities, gaining prominence in machine learning and signal processing.

Node Classification Representation Learning

Hypergraph Structure Inference From Data Under Smoothness Prior

no code implementations27 Aug 2023 Bohan Tang, Siheng Chen, Xiaowen Dong

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

no code implementations3 Nov 2022 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 crucial when a hypergraph topology is not readily available in the datasets.

Collaborative Uncertainty Benefits Multi-Agent Multi-Modal Trajectory Forecasting

no code implementations11 Jul 2022 Bohan Tang, Yiqi Zhong, Chenxin Xu, Wei-Tao Wu, Ulrich Neumann, Yanfeng Wang, Ya zhang, Siheng Chen

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.

regression Task 2 +1

Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational Reasoning

1 code implementation27 Jun 2022 Chenxin Xu, Yuxi Wei, Bohan Tang, Sheng Yin, Ya zhang, Siheng Chen

Demystifying the interactions among multiple agents from their past trajectories is fundamental to precise and interpretable trajectory prediction.

Relational Reasoning Trajectory Prediction

Collaborative Uncertainty in Multi-Agent Trajectory Forecasting

no code implementations NeurIPS 2021 Bohan Tang, Yiqi Zhong, Ulrich Neumann, Gang Wang, Ya zhang, Siheng Chen

2) The results of trajectory forecasting benchmarks demonstrate that the CU-based framework steadily helps SOTA systems improve their performances.

Trajectory Forecasting

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