1 code implementation • 27 Feb 2024 • Wei Xiang, Haoteng Yin, He Wang, Xiaogang Jin
Pedestrian trajectory prediction is the key technology in many applications for providing insights into human behavior and anticipating human future motions.
no code implementations • 28 Dec 2023 • Tianyi Zhang, Haoteng Yin, Rongzhe Wei, Pan Li, Anshumali Shrivastava
We further show that any type of neighborhood overlap-based heuristic can be estimated by a neural network that takes Bloom signatures as input.
no code implementations • 24 Oct 2023 • Rongzhe Wei, Eleonora Kreačić, Haoyu Wang, Haoteng Yin, Eli Chien, Vamsi K. Potluru, Pan Li
Focusing on per-instance differential privacy (pDP), our framework elucidates the potential privacy leakage for each data point in a given training dataset, offering insights into data preprocessing to reduce privacy risks of the synthetic dataset generation via DDMs.
no code implementations • 19 Aug 2023 • Prasita Mukherjee, Haoteng Yin
Model Checking is widely applied in verifying the correctness of complex and concurrent systems against a specification.
1 code implementation • 6 Mar 2023 • Haoteng Yin, Muhan Zhang, Jianguo Wang, Pan Li
Subgraph-based graph representation learning (SGRL) has recently emerged as a powerful tool in many prediction tasks on graphs due to its advantages in model expressiveness and generalization ability.
Ranked #1 on Link Property Prediction on ogbl-ppa
no code implementations • 24 Jul 2022 • Prasita Mukherjee, Haoteng Yin, Susheel Suresh, Tiark Rompf
Model Checking is widely applied in verifying the correctness of complex and concurrent systems against a specification.
1 code implementation • 22 Jul 2022 • Rongzhe Wei, Haoteng Yin, Junteng Jia, Austin R. Benson, Pan Li
Graph neural networks (GNNs) have shown superiority in many prediction tasks over graphs due to their impressive capability of capturing nonlinear relations in graph-structured data.
1 code implementation • ICLR 2022 • Haorui Wang, Haoteng Yin, Muhan Zhang, Pan Li
Graph neural networks (GNN) have shown great advantages in many graph-based learning tasks but often fail to predict accurately for a task-based on sets of nodes such as link/motif prediction and so on.
3 code implementations • 28 Feb 2022 • Haoteng Yin, Muhan Zhang, Yanbang Wang, Jianguo Wang, Pan Li
Subgraph-based graph representation learning (SGRL) has been recently proposed to deal with some fundamental challenges encountered by canonical graph neural networks (GNNs), and has demonstrated advantages in many important data science applications such as link, relation and motif prediction.
Ranked #1 on Link Property Prediction on ogbl-citation2
1 code implementation • 22 Nov 2020 • Haoteng Yin, Yanbang Wang, Pan Li
We want to explain how DE makes GNNs fit for node classification and link prediction.
no code implementations • 13 Mar 2019 • Bing Yu, Haoteng Yin, Zhanxing Zhu
In this U-shaped network, a paired sampling operation is proposed in spacetime domain accordingly: the pooling (ST-Pool) coarsens the input graph in spatial from its deterministic partition while abstracts multi-resolution temporal dependencies through dilated recurrent skip connections; based on previous settings in the downsampling, the unpooling (ST-Unpool) restores the original structure of spatio-temporal graphs and resumes regular intervals within graph sequences.
Ranked #1 on Traffic Prediction on PeMS-M
5 code implementations • 14 Sep 2017 • Bing Yu, Haoteng Yin, Zhanxing Zhu
Timely accurate traffic forecast is crucial for urban traffic control and guidance.
Ranked #2 on Time Series Forecasting on PeMSD7