Sequential Pattern Retrieval: New Representations Inspired by Non-Equilibrium Physics and Associative Memory Models
Abstract
Generating a temporal sequence of outputs from a single output has broad relevance, including in neuroscience and machine learning. Inspired by ideas in non-equilibrium physics and modern associative memory models, we demonstrate new representations of sequence recall. Our findings provide potential strategies to improve the learning of temporal data in state-of-the-art neural networks.
Cite
Text
Du et al. "Sequential Pattern Retrieval: New Representations Inspired by Non-Equilibrium Physics and Associative Memory Models." ICLR 2025 Workshops: NFAM, 2025.Markdown
[Du et al. "Sequential Pattern Retrieval: New Representations Inspired by Non-Equilibrium Physics and Associative Memory Models." ICLR 2025 Workshops: NFAM, 2025.](https://mlanthology.org/iclrw/2025/du2025iclrw-sequential/)BibTeX
@inproceedings{du2025iclrw-sequential,
title = {{Sequential Pattern Retrieval: New Representations Inspired by Non-Equilibrium Physics and Associative Memory Models}},
author = {Du, Matthew and Behera, Agnish Kumar and Rao, Madan and Vaikuntanathan, Suriyanarayanan},
booktitle = {ICLR 2025 Workshops: NFAM},
year = {2025},
url = {https://mlanthology.org/iclrw/2025/du2025iclrw-sequential/}
}