A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection
Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …
generated in great volume by both physical sensors and online processes (virtual sensors) …
Weight-sharing neural architecture search: A battle to shrink the optimization gap
Neural architecture search (NAS) has attracted increasing attention. In recent years,
individual search methods have been replaced by weight-sharing search methods for higher …
individual search methods have been replaced by weight-sharing search methods for higher …
Spatio-temporal graph neural networks for predictive learning in urban computing: A survey
With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
Investigating bi-level optimization for learning and vision from a unified perspective: A survey and beyond
Bi-Level Optimization (BLO) is originated from the area of economic game theory and then
introduced into the optimization community. BLO is able to handle problems with a …
introduced into the optimization community. BLO is able to handle problems with a …
Deep learning on traffic prediction: Methods, analysis, and future directions
X Yin, G Wu, J Wei, Y Shen, H Qi… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic
prediction can assist route planing, guide vehicle dispatching, and mitigate traffic …
prediction can assist route planing, guide vehicle dispatching, and mitigate traffic …
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 …
Airformer: Predicting nationwide air quality in china with transformers
Air pollution is a crucial issue affecting human health and livelihoods, as well as one of the
barriers to economic growth. Forecasting air quality has become an increasingly important …
barriers to economic growth. Forecasting air quality has become an increasingly important …
STDEN: Towards physics-guided neural networks for traffic flow prediction
High-performance traffic flow prediction model designing, a core technology of Intelligent
Transportation System, is a long-standing but still challenging task for industrial and …
Transportation System, is a long-standing but still challenging task for industrial and …
NAS-BERT: task-agnostic and adaptive-size BERT compression with neural architecture search
While pre-trained language models (eg, BERT) have achieved impressive results on
different natural language processing tasks, they have large numbers of parameters and …
different natural language processing tasks, they have large numbers of parameters and …
Traffic prediction using artificial intelligence: review of recent advances and emerging opportunities
Traffic prediction plays a crucial role in alleviating traffic congestion which represents a
critical problem globally, resulting in negative consequences such as lost hours of additional …
critical problem globally, resulting in negative consequences such as lost hours of additional …