Projection techniques are often used to visualize high-dimensional data, allowing users to
better understand the overall structure of multi-dimensional spaces on a 2D screen …

Multidimensional projections are effective techniques for depicting high-dimensional data.
The point patterns created by such techniques, or a technique's visual signature, depend …

Understanding how a machine learning classifier works is an important task in machine
learning engineering. However, doing this is for any classifier in general difficult. We …

We present a method to visually assess the stability of deep learned projections. For this, we
perturb the high-dimensional data by controlled sequences and visualize the resulting …

Self-Supervised Network Projections (SSNP) are dimensionality reduction algorithms that
produce lowdimensional layouts from high-dimensional data. By combining an autoencoder …

Visualization of multidimensional data is a difficult task, for which there are many tools.
Among these tools, dimensionality reduction methods were shown to be particularly helpful …

Projection algorithms such as t-SNE or UMAP are useful for the visualization of high
dimensional data, but depend on hyperparameters which must be tuned carefully …

Dimensionality reduction (DR) is an essential tool for the visualization of high-dimensional
data. The recently proposed Self-Supervised Network Projection (SSNP) method addresses …

In the wake of the revolution brought by Deep Learning, we believe neural networks can be
leveraged as a tool in the service of dimensionality reduction (DR) for understanding large …

Visualization of multidimensional data is a difficult task, for which there are many tools.
Among these tools, dimensionality reduction methods were shown to be particularly helpful …