Deep Learning

CityGuard: Citywide Fire Risk Forecasting Using A Machine Learning Approach

UbiComp 2020

Matrix Factorization for Spatio-Temporal Neural Networks with Applications to Urban Flow Prediction

CIKM 2019, Applied Research Track (acceptance rate: 21.8%)

CityTraffic: Modeling Citywide Traffic via Neural Memorization and Generalization Approach

CIKM 2019, Applied Research Track (acceptance rate: 21.8%)

Urban Traffic Prediction from Spatio-Temporal Data using Deep Meta Learning

[ORAL] KDD 2019, Research Track (acceptance rate: 14.2%)

UrbanFM: Inferring Fine-Grained Urban Flows

KDD 2019, ADS Track (acceptance rate: 20.7%)

DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction

ACL 2019 (acceptance rate: 25.7%)

Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks

Being able to predict the crowd flows in each and every part of a city, especially in irregular regions, is strategically important for traffic control, risk assessment, and public safety. However, it is very challenging because of interactions and …

Flow Prediction in Spatio-Temporal Networks Based on Multitask Deep Learning

Predicting flows (e.g. the traffic of vehicles, crowds and bikes), consisting of the in-out traffic at a node and transitions between different nodes, in a spatio-temporal network plays an important role in transportation systems. However, this is a …

DeepCrime: Attentive Hierarchical Recurrent Networks for Crime Prediction

As urban crimes (e.g., burglary and robbery) negatively impact our everyday life and must be addressed in a timely manner, predict- ing crime occurrences is of great importance for public safety and urban sustainability. However, existing methods do …

Deep Distributed Fusion Network for Air Quality Prediction

KDD 2018, ADS Track (acceptance rate: 14.0%)