Sparse matrix regression (SMR) is a two-dimensional supervised feature selection method that can directly select the features on matrix data. It uses several couples of left and right regression ...

Learning a good graph similarity matrix in data clustering is very crucial. The goal of clustering is to construct a good graph similarity matrix such that the similarity of points between the same ...

In recent years, with the public availability of AI tools, more people have become aware of how closely the inner workings of ...

If there’s one thing that characterizes the Information Age that we find ourselves in today, it is streams of data. However, ...

From the renders of the Nothing Phone 3, there really doesn't seem to be a Glyph interface anywhere on the device's back panel. But that's been confirmed already, so instead, it's a better idea to ...

A biphasic structural plasticity rule interacts with homeostatic synaptic scaling to maintain firing rate homeostasis in neural networks.

The repository includes two primary algorithms: Johnson's algorithm, which is highly efficient for sparse graphs, and the Floyd-Warshall algorithm, which is suitable for dense graphs. Both ...

The official SuiteSparse library: a suite of sparse matrix algorithms authored or co-authored by Tim Davis, Texas A&M University ...

Color indicates time to apoptosis smoothed by nearest neighbors (right). (middle) Similarity and dimensionality analysis for CELL DEATH NANOLIVE: mean cosine similarity scores (first row); RBF CKA ...