Multi-Channel Pattern Reconstruction Through $l$-Directional Associative Memories
Abstract
We consider $L$-directional associative memories, composed of $L$ Hopfield networks, displaying imitative Hebbian intra-network interactions and anti-imitative Hebbian inter-network interactions, where couplings are built over a set of hidden binary patterns. We evaluate the model's performance in reconstructing the whole set of hidden binary patterns when provided with mixtures of noisy versions of these patterns. Our numerical results demonstrate the model's high effectiveness in the reconstruction task for structureless and structured datasets.
Cite
Text
Fachechi et al. "Multi-Channel Pattern Reconstruction Through $l$-Directional Associative Memories." ICLR 2025 Workshops: NFAM, 2025.Markdown
[Fachechi et al. "Multi-Channel Pattern Reconstruction Through $l$-Directional Associative Memories." ICLR 2025 Workshops: NFAM, 2025.](https://mlanthology.org/iclrw/2025/fachechi2025iclrw-multichannel/)BibTeX
@inproceedings{fachechi2025iclrw-multichannel,
title = {{Multi-Channel Pattern Reconstruction Through $l$-Directional Associative Memories}},
author = {Fachechi, Alberto and Agliari, Elena and Alessandrelli, Andrea and Mourao, Paulo Duarte},
booktitle = {ICLR 2025 Workshops: NFAM},
year = {2025},
url = {https://mlanthology.org/iclrw/2025/fachechi2025iclrw-multichannel/}
}