Learning Linear Block Error Correction Codes
Authors: Yoni Choukroun, Lior Wolf
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate our method, we train the proposed architecture with four classes of linear codes: Low-Density Parity Check (LDPC) codes (Gallager, 1962), Polar codes (Arikan, 2008), Reed Solomon codes (Reed & Solomon, 1960) and Bose Chaudhuri Hocquenghem (BCH) codes (Bose & Ray-Chaudhuri, 1960). All the parity check matrices are taken from (Helmling et al., 2019). |
| Researcher Affiliation | Academia | 1The Blavatnik School of Computer Science, Tel Aviv University. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code available at https://github.com/yoni Lc/E2E_DC_ECCT. |
| Open Datasets | Yes | All the parity check matrices are taken from (Helmling et al., 2019). |
| Dataset Splits | No | The hyperparameter search was performed using a validation set as follows. The paper mentions using a validation set and testing, but does not provide specific percentages or sample counts for train/validation/test splits. |
| Hardware Specification | Yes | Training and experiments are performed on a 12GB Ge Force RTX 2080 Ti GPU. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | The Adam optimizer (Kingma & Ba, 2014) is used with 1024 samples per minibatch, for 1K epochs, with 1K minibatches per epoch. We initialized the learning rate to 10 4 coupled with a cosine decay scheduler down to 10 6 at the end of the training. |