166 citations · 655 across the 82 of their papers we have counts for
10 papers · 1 filter
Transversal Pooling Neural Networks
Emily J. King, Dustin G. Mixon, Michael Perlmutter +1
Many learning tasks require stability to small transformations while retaining sensitivity to larger ones. We introduce \emph{transversal pooling neural networks}, which generalize…
Neural collapse in the orthoplex regime
James Alcala, Rayna Andreeva, Vladimir A. Kobzar +4
When training a neural network for classification, the feature vectors of the training set are known to collapse to the vertices of a regular simplex, provided the dimension of…
On the clustering behavior of sliding windows
Boris Alexeev, Wenyan Luo, Dustin G. Mixon +1
Things can go spectacularly wrong when clustering timeseries data that has been preprocessed with a sliding window. We highlight three surprising failures that emerge depending on…
Generalized Neural Collapse for a Large Number of Classes
Jiachen Jiang, Jinxin Zhou, Peng Wang +4
Neural collapse provides an elegant mathematical characterization of learned last layer representations (a.k.a. features) and classifier weights in deep classification models. Such…
Sketch-and-solve approaches to k-means clustering by semidefinite programming
Charles Clum, Dustin G. Mixon, Soledad Villar +1
We introduce a sketch-and-solve approach to speed up the Peng-Wei semidefinite relaxation of k-means clustering. When the data is appropriately separated we identify the k-means op…
Neural collapse with unconstrained features
Dustin G. Mixon, Hans Parshall, Jianzong Pi
Neural collapse is an emergent phenomenon in deep learning that was recently discovered by Papyan, Han and Donoho. We propose a simple "unconstrained features model" in which neura…