On sparse modern hopfield model
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 …
Hopfield model. Like its dense counterpart, the sparse modern Hopfield model equips a …
STanhop: Sparse tandem hopfield model for memory-enhanced time series prediction
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 …
prediction with memory-enhanced capabilities. At the heart of our approach is STanHop, a …
Provably optimal memory capacity for modern hopfield models: Transformer-compatible dense associative memories as spherical codes
We study the optimal memorization capacity of modern Hopfield models and Kernelized
Hopfield Models (KHMs), a transformer-compatible class of Dense Associative Memories …
Hopfield Models (KHMs), a transformer-compatible class of Dense Associative Memories …
[HTML][HTML] The generative neural microdynamics of cognitive processing
DC McNamee - Current Opinion in Neurobiology, 2024 - Elsevier
The entorhinal cortex and hippocampus form a recurrent network that informs many
cognitive processes, including memory, planning, navigation, and imagination. Neural …
cognitive processes, including memory, planning, navigation, and imagination. Neural …
Nonparametric modern hopfield models
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 …
models and utilize this framework to debut an efficient variant. Our key contribution stems …
Uniform memory retrieval with larger capacity for modern hopfield models
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 …
$\mathtt {U\text {-} Hop} $, with enhanced memory capacity. Our key contribution is a …
Modern Hopfield Networks meet Encoded Neural Representations--Addressing Practical Considerations
Content-addressable memories such as Modern Hopfield Networks (MHN) have been
studied as mathematical models of auto-association and storage/retrieval in the human …
studied as mathematical models of auto-association and storage/retrieval in the human …
The Role of Recurrency in Image Segmentation for Noisy and Limited Sample Settings
The biological brain has inspired multiple advances in machine learning. However, most
state-of-the-art models in computer vision do not operate like the human brain, simply …
state-of-the-art models in computer vision do not operate like the human brain, simply …