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Found 5 papers, 1 papers with code
VAC-CNN: A Visual Analytics System for Comparative Studies of Deep Convolutional Neural Networks
The rapid development of Convolutional Neural Networks (CNNs) in recent years has triggered significant breakthroughs in many machine learning (ML) applications.
A Deep Generative Model for Reordering Adjacency Matrices
Depending on the node ordering, an adjacency matrix can highlight distinct characteristics of a graph.
Supporting Analysis of Dimensionality Reduction Results with Contrastive Learning
Dimensionality reduction (DR) is frequently used for analyzing and visualizing high-dimensional data as it provides a good first glance of the data.
A Deep Generative Model for Graph Layout
To provide users with an intuitive way to navigate the layout design space, we present a technique to systematically visualize a graph in diverse layouts using deep generative models.
What Would a Graph Look Like in This Layout? A Machine Learning Approach to Large Graph Visualization
For a given graph, our approach can show what the graph would look like in different layouts and estimate their corresponding aesthetic metrics.
