Manifold learning (ML), also known as nonlinear dimension reduction, is a set of methods to
find the low-dimensional structure of data. Dimension reduction for large, high-dimensional …

Topological data analysis (TDA) can broadly be described as a collection of data analysis
methods that find structure in data. These methods include clustering, manifold estimation …

The high-dimensional data created by high-throughput technologies require visualization
tools that reveal data structure and patterns in an intuitive form. We present PHATE, a …

Hyperspectral (HS) imaging, also known as image spectrometry, is a landmark technique in
geoscience and remote sensing (RS). In the past decade, enormous efforts have been made …

All physical laws are described as mathematical relationships between state variables.
These variables give a complete and non-redundant description of the relevant system …

The population loss of trained deep neural networks often follows precise power-law scaling
relations with either the size of the training dataset or the number of parameters in the …

New energy integration and flexible demand response make smart grid operation scenarios
complex and changeable, which bring challenges to network planning. If every possible …

Datasets with significant proportions of noisy (incorrect) class labels present challenges for
training accurate Deep Neural Networks (DNNs). We propose a new perspective for …

Deep neural networks progressively transform their inputs across multiple processing layers.
What are the geometrical properties of the representations learned by these networks? Here …

Multiplexed RNA sequencing in individual cells is transforming basic and clinical life
sciences,,–. Often, however, tissues must first be dissociated, and crucial information about …