A Foundation Model for Error Correction Codes
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Keywords: Error Correction Codes, Foundation Model
Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2024/AuthorGuide.
TL;DR: We propose for the first time a SOTA foundation Neural error correction decoder capable to decode arbitrary code at any length.
Abstract: In recent years, Artificial Intelligence has undergone a paradigm shift with the rise of foundation models, which are trained on large amounts of data, typically in a self-supervised way, and can then be adapted to a wide range of downstream tasks. In this work, we propose the first foundation model for Error Correction Codes. This model is trained on multiple codes and can then be applied to an unseen code. To enable this, we extend the Transformer architecture in multiple ways: (1) a code-invariant initial embedding, which is also position- and length-invariant, (2) a learned modulation of the attention maps that is conditioned on the Tanner graph, and (3) a length-invariant code-aware noise prediction module that is based on the parity-check matrix. The proposed architecture is trained on multiple short- and medium-length codes and is able to generalize to unseen codes. Its performance on these codes matches and even outperforms the state of the art, despite having a smaller capacity than the leading code-specific transformers. The suggested framework therefore demonstrates, for the first time, the benefits of learning a universal decoder rather than a neural decoder optimized for a given code.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors' identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
Submission Number: 1694