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Found 7 papers, 2 papers with code
InLINE: Inner-Layer Information Exchange for Multi-task Learning on Heterogeneous Graphs
However, MTL introduces the issue of negative transfer, where the training of different tasks interferes with each other as they may focus on different information from the data, resulting in suboptimal performance.
SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks
Quantifying uncertainty is crucial for robust and reliable predictions.
FusionTransNet for Smart Urban Mobility: Spatiotemporal Traffic Forecasting Through Multimodal Network Integration
This study develops FusionTransNet, a framework designed for Origin-Destination (OD) flow predictions within smart and multimodal urban transportation systems.
Information Cascade Prediction under Public Emergencies: A Survey
With the advent of the era of big data, massive information, expert experience, and high-accuracy models bring great opportunities to the information cascade prediction of public emergencies.
Where have you been? A Study of Privacy Risk for Point-of-Interest Recommendation
To better understand and quantify the privacy leakage in mobility data-based ML models, we design a privacy attack suite containing data extraction and membership inference attacks tailored for point-of-interest (POI) recommendation models, one of the most widely used mobility data-based ML models.
Learning to Help Emergency Vehicles Arrive Faster: A Cooperative Vehicle-Road Scheduling Approach
The ever-increasing heavy traffic congestion potentially impedes the accessibility of emergency vehicles (EVs), resulting in detrimental impacts on critical services and even safety of people's lives.
ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs
A number of cross-lingual transfer learning approaches based on neural networks have been proposed for the case when large amounts of parallel text are at our disposal.
