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Convolutional low-rank tensor representation for structural missing traffic data imputation
Recently, low-rank tensor completion (LRTC) methods by exploiting the global low-rankness
of the target tensor have shown great potential for traffic data imputation. However, in real …
of the target tensor have shown great potential for traffic data imputation. However, in real …
EvolutionViT: Multi-objective evolutionary vision transformer pruning under resource constraints
Abstract Vision Transformer (ViT) has emerged as a pivotal model for a variety of visual
tasks, surpassing convolutional neural networks by a substantial margin. However, the …
tasks, surpassing convolutional neural networks by a substantial margin. However, the …
A fast matrix autoregression algorithm based on Tucker decomposition for online prediction of nonlinear real-time taxi-hailing demand without pre-training
Online prediction of real-time taxi-hailing demand generally provides better real-time
decision support for passengers and taxi drivers compared with offline prediction. Current …
decision support for passengers and taxi drivers compared with offline prediction. Current …
Anti-circulant dynamic mode decomposition with sparsity-promoting for highway traffic dynamics analysis
Highway traffic state data collected from a network of sensors can be considered as a high-
dimensional nonlinear dynamical system. In this paper, we develop a novel data-driven …
dimensional nonlinear dynamical system. In this paper, we develop a novel data-driven …
Forecasting high-dimensional spatio-temporal systems from sparse measurements
This paper introduces a new neural network architecture designed to forecast high-
dimensional spatio-temporal data using only sparse measurements. The architecture uses a …
dimensional spatio-temporal data using only sparse measurements. The architecture uses a …
[HTML][HTML] A Regionalization Approach Based on the Comparison of Different Clustering Techniques
For biodiversity conservation and the development of protected areas, it is essential to
create strategic plans that ensure the preservation and sustainable use of natural resources …
create strategic plans that ensure the preservation and sustainable use of natural resources …
Expand and Compress: Exploring Tuning Principles for Continual Spatio-Temporal Graph Forecasting
The widespread deployment of sensing devices leads to a surge in data for spatio-temporal
forecasting applications such as traffic flow, air quality, and wind energy. Although spatio …
forecasting applications such as traffic flow, air quality, and wind energy. Although spatio …
Spatiotemporal regularized tucker decomposition approach for traffic data imputation
W Gong, Z Huang, L Yang - arxiv preprint arxiv:2305.06563, 2023 - arxiv.org
In intelligent transportation systems, traffic data imputation, estimating the missing value from
partially observed data is an inevitable and challenging task. Previous studies have not fully …
partially observed data is an inevitable and challenging task. Previous studies have not fully …
Guaranteed Multidimensional Time Series Prediction via Deterministic Tensor Completion Theory
In recent years, the prediction of multidimensional time series data has become increasingly
important due to its wide-ranging applications. Tensor-based prediction methods have …
important due to its wide-ranging applications. Tensor-based prediction methods have …
[كتاب][B] Low-rank Models for Spatiotemporal Traffic Data Analysis
X Wang - 2024 - search.proquest.com
Smart cities have gained substantial attention due to rapid urbanization and the need to
transform congested urban areas into sustainable, efficient, and safe environments …
transform congested urban areas into sustainable, efficient, and safe environments …