We develop methodology for three-dimensional (3D) radial visualization (RadViz) of multidimensional datasets. The classical two-dimensional (2D) RadViz visualizes multivariate data in the 2D plane by mapping every observation to a point inside unit circle. Our tool, RadViz3D, distributes anchor points uniformly on 3D sphere. show that this uniform distribution provides best with minimal artific...

Convolutional neural networks (CNN) have recently seen tremendous success in various computer vision tasks. However, their application to problems with high dimensional input and output, such as high-resolution image video segmentation or 3D medical imaging, has been limited by factors. Primarily, the training stage, it is necessary store network activations for back-propagation. In these setti...