Moreover, a semantic fusing module is presented to aggregate relation-aware node representations into a compact representation with the learned relation representations.
Ranked #3 on Node Property Prediction on ogbn-mag
Representation learning on heterogeneous graphs aims to obtain low-dimensional node representations that could preserve both node attributes and relation information.
Ranked #6 on Node Property Prediction on ogbn-mag
Graph Convolutional Network (GCN) has been widely applied in transportation demand prediction due to its excellent ability to capture non-Euclidean spatial dependence among station-level or regional transportation demands.
To this end, we propose a structured anomaly detection framework to defend WTNs by modeling the spatio-temporal characteristics of cyber attacks in WTNs.
Given a sequence of sets, where each set contains an arbitrary number of elements, the problem of temporal sets prediction aims to predict the elements in the subsequent set.
Demand for smartwatches has taken off in recent years with new models which can run independently from smartphones and provide more useful features, becoming first-class mobile platforms.
Specifically, the first ConvLSTM unit takes normal traffic flow features as input and generates a hidden state at each time-step, which is further fed into the connected convolutional layer for spatial attention map inference.
Inspired by the fact that most people carry smart wireless devices with them, e. g. smartphones, we propose to use this wireless identifier as a supervisory label.
We build the answer extraction model with state-of-the-art neural networks for single passage reading comprehension, and propose an additional task of passage ranking to help answer extraction in multiple passages.