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Compact Graph Architecture for Speech Emotion Recognition
We propose a deep graph approach to address the task of speech emotion recognition. A compact, efficient and scalable way to repre- sent data is in the form of graphs. -
Neural Collaborative Filtering
The dataset is used for neural collaborative filtering, which is a type of collaborative filtering that uses neural networks to learn the relationships between users and items. -
Graph Neural Networks
Graph Neural Networks (GNNs) have emerged as powerful tools for learning graph-structured data in various domains. -
Diffusion Networks
The dataset used in the paper is a graph signal processing dataset, where the goal is to estimate a parameter vector of interest using a distributed adaptive network. -
Signal-adapted tight frames on graphs
Signal-adapted tight frames on graphs -
Gaussian Processes on Graphs via Spectral Kernel Learning
Gaussian Processes on Graphs via Spectral Kernel Learning
