Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning Linear Block Error Correction Codes
Authors: Yoni Choukroun, Lior Wolf
ICML 2024 | Venue PDF | 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. |