397 citations · 695 across the 41 of their papers we have counts for
7 papers · 1 filter
Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors
Moritz Neun, Christian Eichenberger, Henry Martin +27
The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem…
Adaptive incentive for cross-silo federated learning: A multi-agent reinforcement learning approach
Shijing Yuan, Hongze Liu, Hongtao Lv +4
Cross-silo federated learning (FL) is a typical FL that enables organizations(e.g., financial or medical entities) to train global models on isolated data. Reasonable incentive is…
Multi-task Learning for Sparse Traffic Forecasting
Jiezhang Li, Junjun Li, Yue-Jiao Gong
Accurate traffic prediction is crucial to improve the performance of intelligent transportation systems. Previous traffic prediction tasks mainly focus on small and non-isolated tr…
Seen to Unseen: When Fuzzy Inference System Predicts IoT Device Positioning Labels That Had Not Appeared in Training Phase
Han Xu, Zheming Zuo, Jie Li +1
Situating at the core of Artificial Intelligence (AI), Machine Learning (ML), and more specifically, Deep Learning (DL) have embraced great success in the past two decades. However…
On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach
Jiawei Sun, Ruoxin Chen, Jie Li +3
Graph Contrastive Learning (GCL) has shown promising performance in graph representation learning (GRL) without the supervision of manual annotations. GCL can generate graph-level…
Curvature-based Feature Selection with Application in Classifying Electronic Health Records
Zheming Zuo, Jie Li, Han Xu +1
Disruptive technologies provides unparalleled opportunities to contribute to the identifications of many aspects in pervasive healthcare, from the adoption of the Internet of Thing…
A Framework of Randomized Selection Based Certified Defenses Against Data Poisoning Attacks
Ruoxin Chen, Jie Li, Chentao Wu +2
Neural network classifiers are vulnerable to data poisoning attacks, as attackers can degrade or even manipulate their predictions thorough poisoning only a few training samples. H…