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 …

In Multi-task learning (MTL), a joint model is trained to simultaneously make predictions for
several tasks. Joint training reduces computation costs and improves data efficiency; …

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 …

We consider the problem of time-frequency waveform design for an OFDM dual-functional
radar-communications (DFRC) system that communicates with an OFDM receiver while …

We introduce an Outlier-Efficient Modern Hopfield Model (termed $\mathrm {OutEffHop} $)
and use it to address the outlier inefficiency problem of {training} gigantic transformer-based …

This paper studies an integrated sensing and communication (ISAC) system within a
centralized cell-free massive MIMO (multiple-input multiple-output) network for target …

We propose a two-stage memory retrieval dynamics for modern Hopfield models, termed
$\mathtt {U\text {-} Hop} $, with enhanced memory capacity. Our key contribution is a …

For the energy-efficient deployment of cell-free massive MIMO functionality in a practical
wireless network, the end-to-end (from radio site to the cloud) energy-aware operation is …

Latent variable models are a powerful tool for addressing several tasks in machine learning.
However, the algorithms for learning the parameters of latent variable models are prone to …

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 …