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Found 5 papers, 1 papers with code
An Analytic Solution to Covariance Propagation in Neural Networks
Uncertainty quantification of neural networks is critical to measuring the reliability and robustness of deep learning systems.
Edge Entropy as an Indicator of the Effectiveness of GNNs over CNNs for Node Classification
Graph neural networks (GNNs) extend convolutional neural networks (CNNs) to graph-based data.
Graph Signal Processing and Deep Learning: Convolution, Pooling, and Topology
Deep learning, particularly convolutional neural networks (CNNs), have yielded rapid, significant improvements in computer vision and related domains.
Pooling in Graph Convolutional Neural Networks
Graph convolutional neural networks (GCNNs) are a powerful extension of deep learning techniques to graph-structured data problems.
Detecting gender differences in perception of emotion in crowdsourced data
While we limit ourselves to a single modality (i. e. speech), our framework is applicable to studies of emotion perception from all such loosely annotated data in general.
