We introduce a modern Hopfield network with continuous states and a corresponding
update rule. The new Hopfield network can store exponentially (with the dimension of the …

One of the main challenges in feature learning using Deep Convolutional Neural Networks
(DCNNs) for large-scale face recognition is the design of appropriate loss functions that can …

We introduce the sparse modern Hopfield model as a sparse extension of the modern
Hopfield model. Like its dense counterpart, the sparse modern Hopfield model equips a …

Our goal is to provide an introduction to the study of minimal energy problems, particularly
from the perspective of generating point configurations that provide useful discretizations of …

We present STanHop-Net (Sparse Tandem Hopfield Network) for multivariate time series
prediction with memory-enhanced capabilities. At the heart of our approach is STanHop, a …

There are many ways to generate a set of nodes on the sphere for use in a variety of
problems in numerical analysis. We present a survey of quickly generated point sets on …

Spherical t-designs on 𝕊 d⊂ ℝ d+ 1 S^ d ⊂ R^ d+ 1 provide N nodes for an equal weight
numerical integration rule which is exact for all spherical polynomials of degree at most t …

We present a nonparametric construction for deep learning compatible modern Hopfield
models and utilize this framework to debut an efficient variant. Our key contribution stems …

We study the problem of learning mixtures of linear classifiers under Gaussian covariates.
Given sample access to a mixture of $ r $ distributions on $\mathbb {R}^ n $ of the form …

The inductive bias of a neural network is largely determined by the architecture and the
training algorithm. To achieve good generalization, how to effectively train a neural network …