Deep Neural Collapse (DNC) refers to the surprisingly rigid structure of the data
representations in the final layers of Deep Neural Networks (DNNs). Though the …

Neural collapse ($\mathcal {NC} $) is a phenomenon observed in classification tasks where
top-layer representations collapse into their class means, which become equinorm …

Deep neural networks (DNNs) at convergence consistently represent the training data in the
last layer via a highly symmetric geometric structure referred to as neural collapse. This …

Recent years have witnessed the huge success of deep neural networks (DNNs) in various
tasks of computer vision and text processing. Interestingly, these DNNs with massive …

Neural Collapse (NC) is a fascinating phenomenon that arises during the terminal phase of
training (TPT) of deep neural networks (DNNs). Specifically, for balanced training datasets …

In order to better understand feature learning in neural networks, we propose and study
linear models in tangent feature space where the features are allowed to be transformed …

Deep neural networks (DNNs) exhibit a surprising structure in their final layer known as
neural collapse (NC), and a growing body of works has currently investigated the …

Deep neural networks (DNNs) exhibit a remarkable ability to automatically learn data
representations, finding appropriate features without human input. Here we present a …

Contemporary machine learning algorithms train artificial neural networks by setting network
weights to a single optimized configuration through gradient descent on task-specific …

The widespread use of Deep Neural Networks (DNNs) in various application has
underscored their effectiveness, yet the fundamental principles behind their success largely …