Autost: Efficient neural architecture search for spatio-temporal prediction
Spatio-temporal (ST) prediction (eg crowd flow prediction) is of great importance in a wide
range of smart city applications from urban planning, intelligent transportation and public …
range of smart city applications from urban planning, intelligent transportation and public …
AutoSTG: Neural Architecture Search for Predictions of Spatio-Temporal Graph✱
Spatio-temporal graphs are important structures to describe urban sensory data, eg, traffic
speed and air quality. Predicting over spatio-temporal graphs enables many essential …
speed and air quality. Predicting over spatio-temporal graphs enables many essential …
D-GAN: Deep generative adversarial nets for spatio-temporal prediction
Spatio-temporal (ST) data for urban applications, such as taxi demand, traffic flow, regional
rainfall is inherently stochastic and unpredictable. Recently, deep learning based ST …
rainfall is inherently stochastic and unpredictable. Recently, deep learning based ST …
Self-attention convlstm for spatiotemporal prediction
Spatiotemporal prediction is challenging due to the complex dynamic motion and
appearance changes. Existing work concentrates on embedding additional cells into the …
appearance changes. Existing work concentrates on embedding additional cells into the …
Urban hotspot forecasting via automated spatio-temporal information fusion
Urban hotspot forecasting is one of the most important tasks for resource scheduling and
security in future smart cities. Most previous works employed fixed neural architectures …
security in future smart cities. Most previous works employed fixed neural architectures …
Matrix factorization for spatio-temporal neural networks with applications to urban flow prediction
Predicting urban flow is essential for city risk assessment and traffic management, which
profoundly impacts people's lives and property. Recently, some deep learning models …
profoundly impacts people's lives and property. Recently, some deep learning models …
Dual graph convolution architecture search for travel time estimation
Travel time estimation (TTE) is a crucial task in intelligent transportation systems, which has
been widely used in navigation and route planning. In recent years, several deep learning …
been widely used in navigation and route planning. In recent years, several deep learning …
Cross-city transfer learning for deep spatio-temporal prediction
Spatio-temporal prediction is a key type of tasks in urban computing, eg, traffic flow and air
quality. Adequate data is usually a prerequisite, especially when deep learning is adopted …
quality. Adequate data is usually a prerequisite, especially when deep learning is adopted …
Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction
The prediction of crowd flows is an important urban computing issue whose purpose is to
predict the future number of incoming and outgoing people in regions. Measuring the …
predict the future number of incoming and outgoing people in regions. Measuring the …
Lightweight neural architecture search for temporal convolutional networks at the edge
Neural Architecture Search (NAS) is quickly becoming the go-to approach to optimize the
structure of Deep Learning (DL) models for complex tasks such as Image Classification or …
structure of Deep Learning (DL) models for complex tasks such as Image Classification or …