Atom: Neural Traffic Compression with Spatio-Temporal Graph Neural Networks.
Paul AlmasanKrzysztof RusekShihan XiaoXiang ShiXiangle ChengAlbert Cabellos-AparicioPere Barlet-RosPublished in: GNNet@CoNEXT (2023)
Keyphrases
- neural network
- spatio temporal
- network architecture
- connectionist models
- associative memory
- graph theory
- graph structure
- graph representation
- image compression
- movement patterns
- fuzzy logic
- structured data
- spatial and temporal
- moving objects
- neural model
- traffic flow
- graph mining
- compression algorithm
- pattern recognition
- neural network model
- directed graph
- back propagation
- random walk
- learning rules
- fuzzy systems
- compression scheme
- spatio temporal data
- genetic algorithm
- image sequences
- artificial neural networks
- compression ratio
- neural nets
- neural fuzzy
- neural learning
- connected components
- activation function
- graph model
- multilayer perceptron
- recurrent neural networks
- data compression
- feed forward
- bio inspired
- network traffic
- multi layer
- biologically plausible
- traffic control
- recurrent networks
- computer vision
- social networks
- bipartite graph